{"title":"Neural networks and fuzzy systems Books","description":"","products":[{"product_id":"deep-learning-on-graphs-9781108831741","title":"Deep Learning on Graphs","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDeep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and  healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1)  senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This timely book covers a combination of two active research areas in AI: deep learning and graphs. It serves the pressing need for researchers, practitioners, and students to learn these concepts and algorithms, and apply them in solving real-world problems. Both authors are world-leading experts in this emerging area.' Huan Liu, Arizona State University\u003cbr\u003e'Deep learning on graphs is an emerging and important area of research. This book by Yao Ma and Jiliang Tang covers not only the foundations, but also the frontiers and applications of graph deep learning. This is a must-read for anyone considering diving into this fascinating area.' Shuiwang Ji, Texas A\u0026amp;M University\u003cbr\u003e'The first textbook of Deep Learning on Graphs, with systematic, comprehensive and up-to-date coverage of graph neural networks, autoencoder on graphs, and their applications in natural language processing, computer vision, data mining, biochemistry and healthcare. A valuable book for anyone to learn this hot theme!' Jiawei Han, University of Illinois at Urbana-Champaign\u003cbr\u003e'This book systematically covers the foundations, methodologies, and applications of deep learning on graphs. Especially, it comprehensively introduces graph neural networks and their recent advances. This book is self-contained and nicely structured and thus suitable for readers with different purposes. I highly recommend those who want to conduct research in this area or deploy graph deep learning techniques in practice to read this book.' Charu Aggarwal, Distinguished Research Staff Member at IBM and recipient of the W. Wallace McDowell Award\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Deep Learning on Graphs: An Introduction; 2. Foundation of Graphs; 3. Foundation of Deep Learning; 4. Graph Embedding; 5. Graph Neural Networks; 6. Robust Graph Neural Networks; 7. Scalable Graph Neural Networks; 8. Graph Neural Networks for Complex Graphs; 9. Beyond GNNs: More Deep Models for Graphs; 10. Graph Neural Networks in Natural Language Processing; 11. Graph Neural Networks in Computer Vision; 12. Graph Neural Networks in Data Mining; 13. Graph Neural Networks in Biochemistry and Healthcare; 14. Advanced Topics in Graph Neural Networks; 15. Advanced Applications in Graph Neural Networks.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738338898263,"sku":"9781108831741","price":44.64,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781108831741.jpg?v=1723811946"},{"product_id":"connectionism-and-the-mind-9780631207139","title":"Connectionism and the Mind","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eConnectionism and the Mind\u003c\/i\u003e provides a clear and balanced introduction to connectionist networks and explores theoretical and philosophical implications. Much of this discussion from the first edition has been updated, and three new chapters have been added on the relation of connectionism to recent work on dynamical systems theory, artificial life, and cognitive neuroscience.\u003cbr\u003e \u003cp\u003eRead two of the sample chapters on line:\u003cbr\u003e \u003c\/p\u003e \u003cp\u003eConnectionism and the Dynamical Approach to Cognition:\u003cbr\u003e http:\/\/www.blackwellpublishing.com\/pdf\/bechtel.pdf\u003cbr\u003e \u003c\/p\u003e \u003cp\u003eNetworks, Robots, and Artificial Life:\u003cbr\u003e http:\/\/www.blackwellpublishing.com\/pdf\/bechtel2.pdf\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Much more than just an update, this is a thorough and exciting re-build of the classic text. Excellent new treatments of modularity, dynamics, artificial life, and cognitive neuroscience locate connectionism at the very heart of contemporary debates. A superb combination of detail, clarity, scope, and enthusiasm.\" \u003cb\u003eAndy Clark\u003c\/b\u003e, University of Sussex\u003cbr\u003e \u003cp\u003e\"\u003ci\u003eConnectionism and the Mind\u003c\/i\u003e is an extraordinarily comprehensive and thoughtful review of connectionism, with particular emphasis on recent developments. This new edition will be a valuable primer to those new to the field. But there is more: Bechtel and Abrahamsen's trenchant and even-handed analysis of the conceptual issues that are addressed by connectionist models constitute an important original theoretical contribution to cognitive science.\" \u003cb\u003eJeff Elman\u003c\/b\u003e, University of California at San Diego\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Networks Versus Symbol Systems: Two Approaches To Modeling Cognition 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 A Revolution in the Making? 1\u003c\/p\u003e \u003cp\u003e1.2 Forerunners of Connectionism: Pandemonium and Perceptrons 2\u003c\/p\u003e \u003cp\u003e1.3 The Allure of Symbol Manipulation 7\u003c\/p\u003e \u003cp\u003e1.3.1 From logic to artificial intelligence 7\u003c\/p\u003e \u003cp\u003e1.3.2 From linguistics to information processing 10\u003c\/p\u003e \u003cp\u003e1.3.3 Using artificial intelligence to simulate human information processing 11         \u003c\/p\u003e \u003cp\u003e1.4 The Decline and Re-emergence of Network Models 12\u003c\/p\u003e \u003cp\u003e1.4.1 Problems with perceptrons 12\u003c\/p\u003e \u003cp\u003e1.4.2 Re-emergence: The new connectionism 13\u003c\/p\u003e \u003cp\u003e1.5 New Alliances and Unfinished Business 15\u003c\/p\u003e \u003cp\u003eNotes 17\u003c\/p\u003e \u003cp\u003eSources and Suggested Readings 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Connectionist Architectures 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 The Flavor of Connectionist Processing: A Simulation of Memory Retrieval 19\u003c\/p\u003e \u003cp\u003e2.1.1 Components of the model 20\u003c\/p\u003e \u003cp\u003e2.1.2 Dynamics of the model 22\u003cbr\u003e 2.1.2.1 Memory retrieval in the Jets and Sharks network 22\u003cbr\u003e 2.1.2.2 The equations 23\u003c\/p\u003e \u003cp\u003e2.1.3 Illustrations of the dynamics of the model 24\u003cbr\u003e 2.1.3.1 Retrieving properties from a name 24\u003cbr\u003e 2.1.3.2 Retrieving a name from other properties 26\u003cbr\u003e 2.1.3.3 Categorization and prototype formation 26\u003cbr\u003e 2.1.3.4 Utilizing regularities 28\u003c\/p\u003e \u003cp\u003e2.2 The Design Features of a Connectionist Architecture 29\u003c\/p\u003e \u003cp\u003e2.2.1 Patterns of connectivity 29\u003cbr\u003e 2.2.1.1 Feedforward networks 29\u003cbr\u003e 2.2.1.2 Interactive networks 31\u003c\/p\u003e \u003cp\u003e2.2.2 Activation rules for units 32\u003cbr\u003e 2.2.2.1 Feedforward networks 32\u003cbr\u003e 2.2.2.2 Interactive networks: Hopfield networks and Boltzmann machines 34\u003cbr\u003e 2.2.2.3 Spreading activation vs. interactive connectionist models 37\u003c\/p\u003e \u003cp\u003e2.2.3 Learning principles 38\u003c\/p\u003e \u003cp\u003e2.2.4 Semantic interpretation of connectionist systems 40\u003cbr\u003e 2.2.4.1 Localist networks 41\u003cbr\u003e 2.2.4.2 Distributed networks 41\u003c\/p\u003e \u003cp\u003e2.3 The Allure of the Connectionist Approach 45\u003c\/p\u003e \u003cp\u003e2.3.1 Neural plausibility 45\u003c\/p\u003e \u003cp\u003e2.3.2 Satisfaction of soft constraints 46\u003c\/p\u003e \u003cp\u003e2.3.3 Graceful degradation 48\u003c\/p\u003e \u003cp\u003e2.3.4 Content-addressable memory 49\u003c\/p\u003e \u003cp\u003e2.3.5 Capacity to learn from experience and generalize 51\u003c\/p\u003e \u003cp\u003e2.4 Challenges Facing Connectionist Networks 51\u003c\/p\u003e \u003cp\u003e2.5 Summary 52\u003c\/p\u003e \u003cp\u003eNotes 52\u003c\/p\u003e \u003cp\u003eSources and Recommended Readings 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Learning 54\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Traditional and Contemporary Approaches to Learning 54\u003c\/p\u003e \u003cp\u003e3.1.1 Empiricism 54\u003c\/p\u003e \u003cp\u003e3.1.2 Rationalism 55\u003c\/p\u003e \u003cp\u003e3.1.3 Contemporary cognitive science 56\u003c\/p\u003e \u003cp\u003e3.2 Connectionist Models of Learning 57\u003c\/p\u003e \u003cp\u003e3.2.1 Learning procedures for two-layer feedforward networks 58\u003cbr\u003e 3.2.1.1 Training and testing a network 58\u003cbr\u003e 3.2.1.2 The Hebbian rule 58\u003cbr\u003e 3.2.1.3 The delta rule 60\u003cbr\u003e 3.2.1.4 Comparing the Hebbian and delta rules 67\u003cbr\u003e 3.2.1.5 Limitations of the delta rule: The XOR problem 67\u003c\/p\u003e \u003cp\u003e3.2.2 The backpropagation learning procedure for multi-layered networks 69\u003cbr\u003e 3.2.2.1 Introducing hidden units and backpropagation learning 69\u003cbr\u003e 3.2.2.2 Using backpropagation to solve the XOR problem 74\u003cbr\u003e 3.2.2.3 Using backpropagation to train a network to pronounce words 77\u003cbr\u003e 3.2.2.4 Some drawbacks of using backpropagation 78\u003c\/p\u003e \u003cp\u003e3.2.3 Boltzmann learning procedures for non-layered networks 79\u003c\/p\u003e \u003cp\u003e3.2.4 Competitive learning 80\u003c\/p\u003e \u003cp\u003e3.2.5 Reinforcement learning 81\u003c\/p\u003e \u003cp\u003e3.3 Some Issues Regarding Learning 82\u003c\/p\u003e \u003cp\u003e3.3.1 Are connectionist systems associationist? 82\u003c\/p\u003e \u003cp\u003e3.3.2 Possible roles for innate knowledge 84\u003cbr\u003e 3.3.2.1 Networks and the rationalist–empiricist continuum 84\u003cbr\u003e 3.3.2.2 Rethinking innateness: Connectionism and emergence 85\u003c\/p\u003e \u003cp\u003eNotes 87\u003c\/p\u003e \u003cp\u003eSources and Suggested Readings 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Pattern Recognition and Cognition 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Networks as Pattern Recognition Devices 90\u003c\/p\u003e \u003cp\u003e4.1.1 Pattern recognition in two-layer networks 90\u003c\/p\u003e \u003cp\u003e4.1.2 Pattern recognition in multi-layered networks 93\u003cbr\u003e 4.1.2.1 McClelland and Rumelhart’s interactive activation model of word recognition 93\u003cbr\u003e 4.1.2.2 Evaluating the interactive activation model of word recognition 100\u003c\/p\u003e \u003cp\u003e4.1.3 Generalization and similarity 101\u003c\/p\u003e \u003cp\u003e4.2 Extending Pattern Recognition to Higher Cognition 102\u003c\/p\u003e \u003cp\u003e4.2.1 Smolensky’s proposal: Reasoning in harmony networks 103\u003c\/p\u003e \u003cp\u003e4.2.2 Margolis’s proposal: Cognition as sequential pattern recognition 103\u003c\/p\u003e \u003cp\u003e4.3           Logical Inference as Pattern Recognition 106\u003c\/p\u003e \u003cp\u003e4.3.1 What is it to learn logic? 106\u003c\/p\u003e \u003cp\u003e4.3.2 A network for evaluating validity of arguments 109\u003c\/p\u003e \u003cp\u003e4.3.3 Analyzing how a network evaluates arguments 112\u003c\/p\u003e \u003cp\u003e4.3.4 A network for constructing derivations 115\u003c\/p\u003e \u003cp\u003e4.4 Beyond Pattern Recognition 117\u003c\/p\u003e \u003cp\u003eNotes 118\u003c\/p\u003e \u003cp\u003eSources and Suggested Readings 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Are Rules Required to Process Representations? 120\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Is Language Use Governed by Rules? 120\u003c\/p\u003e \u003cp\u003e5.2 Rumelhart and McClelland’s Model of Past-tense Acquisition 122\u003c\/p\u003e \u003cp\u003e5.2.1 A pattern associator with Wickelfeature encodings 122\u003c\/p\u003e \u003cp\u003e5.2.2 Activation function and learning procedure 126\u003c\/p\u003e \u003cp\u003e5.2.3 Overregularization in a simpler network: The rule of 78 127\u003c\/p\u003e \u003cp\u003e5.2.4 Modeling U-shaped learning 130\u003c\/p\u003e \u003cp\u003e5.2.5 Modeling differences between different verb classes 133\u003c\/p\u003e \u003cp\u003e5.3Pinker and Prince’s Arguments for Rules 135\u003c\/p\u003e \u003cp\u003e5.3.1 Overview of the critique of Rumelhart and McClelland’s model 135\u003c\/p\u003e \u003cp\u003e5.3.2 Putative linguistic inadequacies 136\u003c\/p\u003e \u003cp\u003e5.3.3 Putative behavioral inadequacies 139\u003c\/p\u003e \u003cp\u003e5.3.4 Do the inadequacies reflect inherent limitations of PDP networks? 140\u003c\/p\u003e \u003cp\u003e5.4 Accounting for the U-shaped Learning Function 141\u003c\/p\u003e \u003cp\u003e5.4.1 The role of input for children 142\u003c\/p\u003e \u003cp\u003e5.4.2 The role of input for networks: The rule of 78 revisited 146\u003c\/p\u003e \u003cp\u003e5.4.3 Plunkett and Marchman’s simulations of past-tense acquisition 148\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 152\u003c\/p\u003e \u003cp\u003eNotes 153\u003c\/p\u003e \u003cp\u003eSources and Suggested Readings 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Are Syntactically Structured Representations Needed? 156\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Fodor and Pylyshyn’s Critique: The Need for Symbolic Representations with Constituent Structure 156\u003c\/p\u003e \u003cp\u003e6.1.1 The need for compositional syntax and semantics 156\u003c\/p\u003e \u003cp\u003e6.1.2 Connectionist representations lack compositionality 158\u003c\/p\u003e \u003cp\u003e6.1.3 Connectionism as providing mere implementation 160\u003c\/p\u003e \u003cp\u003e6.2 First Connectionist Response: Explicitly Implementing Rules and Representations 163\u003c\/p\u003e \u003cp\u003e6.2.1 Implementing a production system in a network 163\u003c\/p\u003e \u003cp\u003e6.2.2 The variable binding problem 165\u003c\/p\u003e \u003cp\u003e6.2.3 Shastri and Ajjanagadde’s connectionist model of variable binding 166\u003c\/p\u003e \u003cp\u003e6.3Second Connectionist Response: Implementing Functionally Compositional Representations 170\u003c\/p\u003e \u003cp\u003e6.3.1 Functional vs. concatenative compositionality 170\u003c\/p\u003e \u003cp\u003e6.3.2 Developing compressed representations using Pollack’s RAAM networks 171\u003c\/p\u003e \u003cp\u003e6.3.3 Functional compositionality of compressed representations 175\u003c\/p\u003e \u003cp\u003e6.3.4 Performing operations on compressed representations 177\u003c\/p\u003e \u003cp\u003e6.4 Third Connectionist Response: Employing Procedural Knowledge with External Symbols 178\u003c\/p\u003e \u003cp\u003e6.4.1 Temporal dependencies in processing language 179\u003c\/p\u003e \u003cp\u003e6.4.2 Achieving short-term memory with simple recurrent networks 180\u003c\/p\u003e \u003cp\u003e6.4.3 Elman’s first study: Learning grammatical categories 181\u003c\/p\u003e \u003cp\u003e6.4.4 Elman’s second study: Respecting dependency relations 184\u003c\/p\u003e \u003cp\u003e6.4.5 Christiansen’s extension: Pushing the limits of SRNs 187\u003c\/p\u003e \u003cp\u003e6.5 Using External Symbols to Provide Exact Symbol Processing 190\u003c\/p\u003e \u003cp\u003e6.6 Clarifying the Standard: Systematicity and Degree of Generalizability 194\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 197\u003c\/p\u003e \u003cp\u003eNotes 198\u003c\/p\u003e \u003cp\u003eSources and Suggested Readings 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Simulating Higher Cognition: a Modular Architecture For Processing Scripts 200\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Overview of Scripts 200\u003c\/p\u003e \u003cp\u003e7.2 Overview of Miikkulainen’s DISCERN System 201\u003c\/p\u003e \u003cp\u003e7.3Modular Connectionist Architectures 203\u003c\/p\u003e \u003cp\u003e7.4 FGREP: An Architecture that Allows the System to Devise Its Own Representations 206\u003c\/p\u003e \u003cp\u003e7.4.1 Why FGREP? 206\u003c\/p\u003e \u003cp\u003e7.4.2 Exploring FGREP in a simple sentence parser 208\u003c\/p\u003e \u003cp\u003e7.4.3 Exploring representations for words in categories 210\u003c\/p\u003e \u003cp\u003e7.4.4 Moving to multiple modules: The DISCERN system 212\u003c\/p\u003e \u003cp\u003e7.5 A Self-organizing Lexicon Using Kohonen Feature Maps 212\u003c\/p\u003e \u003cp\u003e7.5.1 Innovations in lexical design 212\u003c\/p\u003e \u003cp\u003e7.5.2 Using Kohonen feature maps in DISCERN’s lexicon 213\u003cbr\u003e 7.5.2.1 Orthography: From high-dimensional vector representations to map units 213\u003cbr\u003e 7.5.2.2 Associative connections: From the orthographic map to the semantic map 216\u003cbr\u003e 7.5.2.3 Semantics: From map unit to high-dimensional vector representations 216\u003cbr\u003e 7.5.2.4 Reversing direction: From semantic to orthographic representations 216\u003c\/p\u003e \u003cp\u003e7.5.3 Advantages of Kohonen feature maps 216\u003c\/p\u003e \u003cp\u003e7.6 Encoding and Decoding Stories as Scripts 217\u003c\/p\u003e \u003cp\u003e7.6.1 Using recurrent FGREP modules in DISCERN 217\u003c\/p\u003e \u003cp\u003e7.6.2 Using the Sentence Parser and Story Parser to encode stories 218\u003c\/p\u003e \u003cp\u003e7.6.3 Using the Story Generator and Sentence Generator to paraphrase stories 221\u003c\/p\u003e \u003cp\u003e7.6.4 Using the Cue Former and Answer Producer to answer questions 223\u003c\/p\u003e \u003cp\u003e7.7 A Connectionist Episodic Memory 223\u003c\/p\u003e \u003cp\u003e7.7.1 Making Kohonen feature maps hierarchical 223\u003c\/p\u003e \u003cp\u003e7.7.2 How role-binding maps become self-organized 225\u003c\/p\u003e \u003cp\u003e7.7.3 How role-binding maps become trace feature maps 225\u003c\/p\u003e \u003cp\u003e7.8 Performance: Paraphrasing Stories and Answering Questions 228\u003c\/p\u003e \u003cp\u003e7.8.1 Training and testing DISCERN 228\u003c\/p\u003e \u003cp\u003e7.8.2 Watching DISCERN paraphrase a story 229\u003c\/p\u003e \u003cp\u003e7.8.3 Watching DISCERN answer questions 229\u003c\/p\u003e \u003cp\u003e7.9 Evaluating DISCERN 231\u003c\/p\u003e \u003cp\u003e7.10 Paths Beyond the First Decade of Connectionism 233\u003c\/p\u003e \u003cp\u003eNotes 234\u003c\/p\u003e \u003cp\u003eSources and Suggested Readings 234\u003c\/p\u003e \u003cp\u003e8 Connectionism and the Dynamical Approach to\u003c\/p\u003e \u003cp\u003eCognition 235\u003c\/p\u003e \u003cp\u003e8.1 Are We on the Road to a Dynamical Revolution? 235\u003c\/p\u003e \u003cp\u003e8.2 Basic Concepts of DST: The Geometry of Change 237\u003c\/p\u003e \u003cp\u003e8.2.1 Trajectories in state space: Predators and prey 237\u003c\/p\u003e \u003cp\u003e8.2.2 Bifurcation diagrams and chaos 240\u003c\/p\u003e \u003cp\u003e8.2.3 Embodied networks as coupled dynamical systems 242\u003c\/p\u003e \u003cp\u003e8.3Using Dynamical Systems Tools to Analyze Networks 243\u003c\/p\u003e \u003cp\u003e8.3.1 Discovering limit cycles in network controllers for robotic insects 244\u003c\/p\u003e \u003cp\u003e8.3.2 Discovering multiple attractors in network models of reading 246\u003cbr\u003e 8.3.2.1 Modeling the semantic pathway 248\u003cbr\u003e 8.3.2.2 Modeling the phonological pathway 249\u003c\/p\u003e \u003cp\u003e8.3.3 Discovering trajectories in SRNs for sentence processing 253\u003c\/p\u003e \u003cp\u003e8.3.4 Dynamical analyses of learning in networks 256\u003c\/p\u003e \u003cp\u003e8.4 Putting Chaos to Work in Networks 257\u003c\/p\u003e \u003cp\u003e8.4.1 Skarda and Freeman’s model of the olfactory bulb 257\u003c\/p\u003e \u003cp\u003e8.4.2 Shifting interpretations of ambiguous displays 260\u003c\/p\u003e \u003cp\u003e8.5 Is Dynamicism a Competitor to Connectionism? 264\u003c\/p\u003e \u003cp\u003e8.5.1 Van Gelder and Port’s critique of classic connectionism 264\u003c\/p\u003e \u003cp\u003e8.5.2 Two styles of modeling 265\u003c\/p\u003e \u003cp\u003e8.5.3 Mechanistic versus covering-law explanations 266\u003c\/p\u003e \u003cp\u003e8.5.4 Representations: Who needs them? 270\u003c\/p\u003e \u003cp\u003e8.6 Is Dynamicism Complementary to Connectionism? 276\u003c\/p\u003e \u003cp\u003e8.7 Conclusion 280\u003c\/p\u003e \u003cp\u003eNotes 280\u003c\/p\u003e \u003cp\u003eSources and Suggested Readings 281\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Networks, Robots, and Artificial Life 282\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Robots and the Genetic Algorithm 282\u003c\/p\u003e \u003cp\u003e9.1.1 The robot as an artificial lifeform 282\u003c\/p\u003e \u003cp\u003e9.1.2 The genetic algorithm for simulated evolution 283\u003c\/p\u003e \u003cp\u003e9.2 Cellular Automata and the Synthetic Strategy 284\u003c\/p\u003e \u003cp\u003e9.2.1 Langton’s vision: The synthetic strategy 284\u003c\/p\u003e \u003cp\u003e9.2.2 Emergent structures from simple beings: Cellular automata 286\u003c\/p\u003e \u003cp\u003e9.2.3 Wolfram’s four classes of cellular automata 288\u003c\/p\u003e \u003cp\u003e9.2.4 Langton and l at the edge of chaos 289\u003c\/p\u003e \u003cp\u003e9.3Evolution and Learning in Food-seekers 291\u003c\/p\u003e \u003cp\u003e9.3.1 Overview and study 1: Evolution without learning 291\u003c\/p\u003e \u003cp\u003e9.3.2 The Baldwin effect and study 2: Evolution with learning 293\u003c\/p\u003e \u003cp\u003e9.4 Evolution and Development in Khepera 295\u003c\/p\u003e \u003cp\u003e9.4.1 Introducing Khepera 295\u003c\/p\u003e \u003cp\u003e9.4.2 The development of phenotypes from genotypes 296\u003c\/p\u003e \u003cp\u003e9.4.3 The evolution of genotypes 298\u003c\/p\u003e \u003cp\u003e9.4.4 Embodied networks: Controlling real robots 298\u003c\/p\u003e \u003cp\u003e9.5 The Computational Neuroethology of Robots 300\u003c\/p\u003e \u003cp\u003e9.6 When Philosophers Encounter Robots 301\u003c\/p\u003e \u003cp\u003e9.6.1 No Cartesian split in embodied agents? 301\u003c\/p\u003e \u003cp\u003e9.6.2 No representations in subsumption architectures? 302\u003c\/p\u003e \u003cp\u003e9.6.3 No intentionality in robots and Chinese rooms? 303\u003c\/p\u003e \u003cp\u003e9.6.4 No armchair when Dennett does philosophy? 304\u003c\/p\u003e \u003cp\u003e9.7 Conclusion 305\u003c\/p\u003e \u003cp\u003eSources and Suggested Readings 305\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Connectionism and the Brain 306\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Connectionism Meets Cognitive Neuroscience 306\u003c\/p\u003e \u003cp\u003e10.2 Four Connectionist Models of Brain Processes 309\u003c\/p\u003e \u003cp\u003e10.2.1 What\/Where streams in visual processing 309\u003c\/p\u003e \u003cp\u003e10.2.2 The role of the hippocampus in memory 313\u003cbr\u003e 10.2.2.1 The basic design and functions of the hippocampal system 313\u003cbr\u003e 10.2.2.2 Spatial navigation in rats 315\u003cbr\u003e 10.2.2.3 Spatial versus declarative memory accounts 316\u003cbr\u003e 10.2.2.4 Declarative memory in humans and monkeys 318\u003c\/p\u003e \u003cp\u003e10.2.3 Simulating dyslexia in network models of reading 323\u003cbr\u003e 10.2.3.1 Double dissociations in dyslexia 323\u003cbr\u003e 10.2.3.2 Modeling deep dyslexia 327\u003cbr\u003e 10.2.3.3 Modeling surface dyslexia 331\u003cbr\u003e 10.2.3.4 Two pathways versus dual routes 335\u003c\/p\u003e \u003cp\u003e10.2.4 The computational power of modular structure in neocortex 338\u003c\/p\u003e \u003cp\u003e10.3The Neural Implausibility of Many Connectionist Models 341\u003c\/p\u003e \u003cp\u003e10.3.1 Biologically implausible aspects of connectionist networks 342\u003c\/p\u003e \u003cp\u003e10.3.2 How important is neurophysiological plausibility? 343\u003c\/p\u003e \u003cp\u003e10.4 Whither Connectionism? 346\u003c\/p\u003e \u003cp\u003eNotes 347\u003c\/p\u003e \u003cp\u003eSources and Suggested Readings 348\u003cbr\u003e Appendix A: Notation 349\u003cbr\u003e Appendix B: Glossary 350\u003cbr\u003e Bibliography 363\u003cbr\u003e Name Index 384\u003cbr\u003e Subject Index 395\u003c\/p\u003e","brand":"John Wiley and Sons Ltd","offers":[{"title":"Default Title","offer_id":48865438990679,"sku":"9780631207139","price":34.15,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780631207139.jpg?v=1722274052"},{"product_id":"grokking-machine-learning-9781617295911","title":"Grokking Machine Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIt's time to dispel the myth that machine learning is difficult. \u003ci\u003eGrokking Machine Learning\u003c\/i\u003e teaches you how to apply ML to your projects using only standard Python code and high school-level math. 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This revolutionary data analysis approach is behind everything from recommendation systems to self-driving cars, and is transforming industries from finance to art.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eLuis G. Serrano \u003c\/b\u003ehas worked as the Head of Content for Artificial Intelligence at Udacity and as a Machine Learning Engineer at Google, where he worked on the YouTube recommendations system. He holds a PhD in mathematics from the University of Michigan, a Bachelor and Masters from the University of Waterloo, and worked as a postdoctoral researcher at the University of Quebec at Montreal. He shares his machine learning expertise on a YouTube channel with over 2 million views and 35 thousand subscribers, and is a frequent speaker at artificial intelligence and data science conferences.\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48867783868759,"sku":"9781617295911","price":43.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617295911.jpg?v=1722284945"},{"product_id":"grokking-deep-reinforcement-learning-9781617295454","title":"Grokking Deep Reinforcement Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003ci\u003e \u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eWritten for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e \u003cp\u003eGrokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e  \u003cp\u003e\u003c\/p\u003e \u003cp\u003eWe all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. 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The system perceives the environment, interprets the results of its past decisions and uses this information to optimize its behavior for maximum long-term return.\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48867783901527,"sku":"9781617295454","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617295454.jpg?v=1722284945"},{"product_id":"classic-computer-science-problems-in-java-9781617297601","title":"Classic Computer Science Problems in Java","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eSharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You’ll work through a series of exercises based in computer science fundamentals that are designed to improve your software development abilities, improve your understanding of artificial intelligence, and even prepare you to ace an interview.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eClassic Computer Science Problems in Java will teach you techniques to solve common-but-tricky programming issues. You’ll explore foundational coding methods, fundamental algorithms, and artificial intelligence topics, all through code-centric Java tutorials and computer science exercises. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your \"new\" problems!\u003c\/p\u003e \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKey Features\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e·   Recursion, memorization, bit manipulation\u003c\/p\u003e \u003cp\u003e·   Search algorithms\u003c\/p\u003e \u003cp\u003e·   Constraint-satisfaction problems\u003c\/p\u003e \u003cp\u003e·   Graph algorithms\u003c\/p\u003e \u003cp\u003e·   K-means clustering\u003c\/p\u003e \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFor intermediate Java programmers.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eAbout the technology \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn any computer science classroom you’ll find a set of tried-and-true algorithms, techniques, and coding exercises. 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This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R.       what's inside      \u003cul\u003e\n\u003cli\u003eImage classification and image segmentation\u003c\/li\u003e\n\u003cli\u003eTime series forecasting\u003c\/li\u003e\n\u003cli\u003eText classification and machine translation\u003c\/li\u003e\n\u003cli\u003eText generation, neural style transfer, and image generation\u003c\/li\u003e\n\u003c\/ul\u003e       about the reader   For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.       ","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48867861758295,"sku":"9781633439849","price":41.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781633439849.jpg?v=1722285316"},{"product_id":"math-for-deep-learning-what-you-need-to-know-to-understand-neural-networks-9781718501904","title":"Math For Deep Learning: What You Need to Know to","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWith Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You'll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You'll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network. In addition you'll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad\/Adadelta.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"An excellent resource for anyone looking to gain a solid foundation in the mathematics underlying deep learning algorithms. The book is accessible, well-organized, and provides clear explanations and practical examples of key mathematical concepts. I highly recommend it to anyone interested in this field.\"\u003cbr\u003e\u003cb\u003e—Daniel Gutierrez, insideBIGDATA\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\"Ronald T. Kneusel has written a handy and compact guide to the mathematics of deep learning. It will be a well-worn reference for equations and algorithms for the student, scientist, and practitioner of neural networks and machine learning. Complete with equations, figures and even sample code in Python, this book is a wonderful mathematical introduction for the reader.\"\u003cbr\u003e\u003cb\u003e—David S. Mazel, Senior Engineer, Regulus-Group\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\"What makes \u003ci\u003eMath for Deep Learning\u003c\/i\u003e a stand-out, is that it focuses on providing a sufficient mathematical foundation for deep learning, rather than attempting to cover all of deep learning, and introduce the needed math along the way. Those eager to master deep learning are sure to benefit from this foundation-before-house approach.\"\u003cbr\u003e\u003cb\u003e\u003cb\u003e—\u003c\/b\u003eEd Scott, Ph.D., Solutions Architect \u0026amp; IT Enthusiast\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eIntroduction\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eChapter 1:\u003c\/b\u003e Setting the Stage\u003cbr\u003e\u003cb\u003eChapter 2:\u003c\/b\u003e Probability\u003cbr\u003e\u003cb\u003eChapter 3:\u003c\/b\u003e More Probability\u003cbr\u003e\u003cb\u003eChapter 4:\u003c\/b\u003e Statistics\u003cbr\u003e\u003cb\u003eChapter 5:\u003c\/b\u003e Linear Algebra\u003cbr\u003e\u003cb\u003eChapter 6:\u003c\/b\u003e More Linear Algebra\u003cbr\u003e\u003cb\u003eChapter 7:\u003c\/b\u003e Differential Calculus\u003cbr\u003e\u003cb\u003eChapter 8:\u003c\/b\u003e Matrix Calculus\u003cbr\u003e\u003cb\u003eChapter 9:\u003c\/b\u003e Data Flow in Neural Networks\u003cbr\u003e\u003cb\u003eChapter 10:\u003c\/b\u003e Backpropagation\u003cbr\u003e\u003cb\u003eChapter 11:\u003c\/b\u003e Gradient Descent\u003cbr\u003e\u003cb\u003eAppendix:\u003c\/b\u003e Going Further","brand":"No Starch Press,US","offers":[{"title":"Default Title","offer_id":48868102504791,"sku":"9781718501904","price":35.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781718501904.jpg?v=1722286399"},{"product_id":"pytorch-recipes-9781484289242","title":"PyTorch Recipes","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eLearn how to use PyTorch to build neural network models using code snippets updated for this second edition. This book includes new chapters covering topics such as distributed PyTorch modeling, deploying PyTorch models in production, and developments around PyTorch with updated code.   You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as LSTMs, and RNNs. Next, you'll explore probability distribution concepts using PyTorch, as well as supervised and unsupervised algorithms with PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural networks using PyTorch. This new edition covers also topics such as Scorch, a compatible module equivalent to the Scikit machine learning library, model quantization to reduce parameter size, and preparing a model for deployment within a production system. Distributed parallel processing for bala\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“The book covers all important facets of neural network implementation and modeling, and could definitely be useful to students and developers keen for an in-depth look at how to build models using PyTorch, or how to engineer particular neural network features using this platform.” (Mariana Damova, Computing Reviews, July 24, 2023)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eChapter 1: Introduction to PyTorch, Tensors, and Tensor Operations\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eChapter Goal: This chapter is to understand what is PyTorch and its basic building blocks.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eChapter 2: Probability Distributions Using PyTorch\u003c\/p\u003e\u003cp\u003eChapter Goal: This chapter aims at covering different distributions compatible with PyTorch for data analysis.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eChapter 3: Neural Networks Using PyTorch\u003c\/p\u003e\u003cp\u003eChapter Goal: This chapter explains the use of PyTorch to develop a neural network model and optimize the model.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eChapter 4: Deep Learning (CNN and RNN) Using PyTorch\u003c\/p\u003e\u003cp\u003eChapter Goal: This chapter explains the use of PyTorch to train deep neural networks for complex datasets.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eChapter 5: Language Modeling Using PyTorch\u003c\/p\u003e\u003cp\u003eChapter Goal: In this chapter, we are going to use torch text for natural language processing, pre-processing, and feature engineering. \u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eChapter 6: Supervised Learning Using PyTorch\u003c\/p\u003e\u003cp\u003eGoal: This chapter explains how supervised learning algorithms implementation with PyTorch.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eChapter 7: Fine Tuning Deep Learning Models using PyTorch\u003c\/p\u003e\u003cp\u003eGoal: This chapter explains how to Fine Tuning Deep Learning Models using the PyTorch framework.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eChapter 8: Distributed PyTorch Modeling\u003c\/p\u003e\u003cp\u003eChapter Goal: This chapter explains the use of parallel processing using the PyTorch framework.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eChapter 9: Model Optimization Using Quantization Methods\u003c\/p\u003e\u003cp\u003eChapter Goal: This chapter explains the use of quantization methods to optimize the PyTorch models and hyperparameter tuning with ray tune. \u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eChapter 10: Deploying PyTorch Models in Production\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eChapter Goal: In this chapter we are going to use torch serve, to deploy the PyTorch models into production.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eChapter 11: PyTorch for Audio\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eChapter Goal: In this chapter torch audio will be used for audio resampling, data augmentation, features extractions, model training, and pipeline development.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eChapter 12: PyTorch for Image\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eChapter Goal: This chapter aims at using Torchvision for image transformations, pre-processing, feature engineering, and model training.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eChapter 13: Model Explainability using Captum\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eChapter Goal: In this chapter, we are going to use the captum library for model interpretability to explain the model as if you are explaining the model to a 5-year-old.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eChapter 14: Scikit Learn Model compatibility using Skorch\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eChapter Goal: In this chapter, we are going to use skorch which is a high-level library for PyTorch that provides full sci-kit learn compatibility.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48885829828951,"sku":"9781484289242","price":33.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781484289242.jpg?v=1722537848"},{"product_id":"computational-intelligence-for-movement-sciences-neural-networks-and-other-emerging-techniques-9781591408369","title":"Computational Intelligence for Movement Sciences:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eRecent years have seen many new developments in computational intelligence (CI) techniques and, consequently, this has led to an exponential increase in the number of applications in a variety of areas, including: engineering, finance, social and biomedical. In particular, CI techniques are increasingly being used in biomedical and human movement areas because of the complexity of the biological systems, as well as the limitations of the existing quantitative techniques in modelling. \"\"Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques\"\" contains information regarding state-of-the-art research outcomes and cutting-edge technology from leading scientists and researchers working on various aspects of the human movement. Readers of this book will gain an insight into this field, as well as access to pertinent information, which they will be able to use for continuing research in this area.","brand":"IGI Global","offers":[{"title":"Default Title","offer_id":48886489055575,"sku":"9781591408369","price":66.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781591408369.jpg?v=1722540296"},{"product_id":"succeeding-with-ai-9781617296932","title":"Succeeding with AI","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThe big challenge for a successful AI project isn’t deciding which problems you can solve. It’s deciding which problems you should solve. \u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eIn \u003ci\u003eManaging Successful AI Projects\u003c\/i\u003e, author and AI consultant Veljko Krunic reveals secrets for succeeding in AI that he developed with Fortune 500 companies, early-stage start-ups, and other business across multiple industries.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKey Features\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e·   Selecting the right AI project to meet specific business goals\u003c\/p\u003e \u003cp\u003e·   Economizing resources to deliver the best value for money\u003c\/p\u003e \u003cp\u003e·   How to measure the success of your AI efforts in the business terms\u003c\/p\u003e \u003cp\u003e·   Predict if you are you on the right track to deliver your intended business results\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003eFor executives, managers, team leaders, and business-focused data scientists. No specific technical knowledge or programming skills required.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eAbout the technology \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCompanies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. \u003ci\u003eManaging Successful AI Projects\u003c\/i\u003e sets out a blueprint for AI projects to ensure they are predictable, successful, and profitable. It’s filled with practical techniques for running data science programs that ensure they’re cost effective and focused on the right business goals.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003e\u003cb\u003eVeljko Krunic\u003c\/b\u003e is an independent data science consultant who has worked with companies that range from start-ups to Fortune 10 enterprises. He holds a PhD in Computer Science and an MS in Engineering Management, both from the University of Colorado at Boulder. He is also a Six Sigma Master Black Belt.\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":48886900195671,"sku":"9781617296932","price":37.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781617296932.jpg?v=1722542090"},{"product_id":"meta-heuristic-optimization-techniques-applications-in-engineering-9783110716177","title":"Meta-heuristic Optimization Techniques:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book offers a thorough overview of the most popular and researched meta-heuristic optimization techniques and nature-inspired algorithms. Their wide applicability makes them a hot research topic and an effi cient tool for the solution of complex optimization problems in various fi elds of sciences, engineering, and in numerous industries. \u003c\/p\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48889048334679,"sku":"9783110716177","price":106.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783110716177.jpg?v=1722552437"},{"product_id":"neural-networks-and-deep-learning-a-textbook-9783031296413","title":"Neural Networks and Deep Learning: A Textbook","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Deep learning methods for various data domains, such as text, images, and graphs are presented in detail. The chapters of this book span three categories:\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eThe basics of neural networks:\u003c\/b\u003e The backpropagation algorithm is discussed in Chapter 2.\u003c\/p\u003e\u003cp\u003eMany traditional machine learning models can be understood as special cases of neural networks. Chapter 3 explores the connections between traditional machine learning and neural networks. Support vector machines, linear\/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eFundamentals of neural networks:\u003c\/b\u003e  A detailed discussion of training and regularization is provided in Chapters 4 and 5. Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAdvanced topics in neural networks:\u003c\/b\u003e  Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks. Several advanced topics like deep reinforcement learning, attention mechanisms, transformer networks, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 11 and 12.\u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eThe textbook is written for graduate students and upper under graduate level students. Researchers and practitioners working within this related field will want to purchase this as well.\u003c\/p\u003e\u003cp\u003eWhere possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.\u003c\/p\u003eThe second edition is substantially reorganized and expanded with separate chapters on backpropagation and graph neural networks. Many chapters have been significantly revised over the first edition.\u003cp\u003e\u003c\/p\u003e\u003cp\u003eGreater focus is placed on modern deep learning ideas such as attention mechanisms, transformers, and pre-trained language models.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAn Introduction to Neural Networks.- The Backpropagation Algorithm.- Machine Learning with Shallow Neural Networks.- Deep Learning: Principles and Training Algorithms.- Teaching a Deep Neural Network to Generalize.- Radial Basis Function Networks.- Restricted Boltzmann Machines.- Recurrent Neural Networks.- Convolutional Neural Networks.- Graph Neural Networks.- Deep Reinforcement Learning.- Advanced Topics in Deep Learning.\u003c\/p\u003e\u003cbr\u003e","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49084755870039,"sku":"9783031296413","price":50.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031296413.jpg?v=1725553238"},{"product_id":"elements-of-causal-inference-9780262037310","title":"Elements of Causal Inference","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"MIT Press Ltd","offers":[{"title":"Default Title","offer_id":49400685592919,"sku":"9780262037310","price":38.7,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780262037310.jpg?v=1730471292"},{"product_id":"neural-and-adaptive-systems-9780471351672","title":"Neural and Adaptive Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eLike no other text in this field, authors Jose C. Principe, Neil R. Euliano, and W. Curt Lefebvre have written a unique and innovative text unifying the concepts of neural networks and adaptive filters into a common framework.\u003cbr\u003e \u003cbr\u003e   \u003cp\u003eThe text is suitable for senior\/graduate courses in neural networks and adaptive filters. It offers over 200 fully functional simulations (with instructions) to demonstrate and reinforce key concepts and help the reader develop an intuition about the behavior of adaptive systems with real data. This creates a powerful self-learning environment highly suitable for the professional audience.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eChapter 1 Data Fitting with Linear Models 1\u003cbr\u003e \u003cbr\u003e Chapter 2 Pattern Recognition 68\u003cbr\u003e \u003cbr\u003e Chapter 3 Multilayer Perceptrons 100\u003cbr\u003e \u003cbr\u003e Chapter 4 Designing and Training MLPS 173\u003cbr\u003e \u003cbr\u003e Chapter 5 Function Approximation with MLPs, Radial Basis Functions, and Support Vector Machines 223\u003cbr\u003e \u003cbr\u003e Chapter 6 Hebbian Learning and Principal Component Analysis 279\u003cbr\u003e \u003cbr\u003e Chapter 7 Competitive and Kohonen Networks 333\u003cbr\u003e \u003cbr\u003e Chapter 8 Principles of Digital Signal Processing 364\u003cbr\u003e \u003cbr\u003e Chapter 9 Adaptive Filters 429\u003cbr\u003e \u003cbr\u003e Chapter 10 Temporal Processing with Neural Networks 473\u003cbr\u003e \u003cbr\u003e Chapter 11 Training and Using Recurrent Networks 525\u003c\/p\u003e \u003cp\u003eAppendix A Elements of Linear Algebra and Pattern Recognition 589\u003c\/p\u003e \u003cp\u003eAppendix B NeuroSolutions Tutorial 613\u003c\/p\u003e \u003cp\u003eAppendix C Data Directory 637\u003cbr\u003e \u003cbr\u003e Glossary 639\u003cbr\u003e \u003cbr\u003e Index 647\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402571129175,"sku":"9780471351672","price":129.15,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471351672.jpg?v=1730480794"},{"product_id":"minds-and-machines-9781405113489","title":"Minds and Machines","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eExamines different kinds of models and investigates some of the basic properties of connectionism in the context of synthetic psychology, including accounts of how the internal structure of connectionist networks can be interpreted. This title investigates basic properties of connectionism in the context of synthetic psychology.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"In this remarkable book, Dawson refines and develops \u003ci\u003esynthetic\u003c\/i\u003e psychology – an approach to explaining mental capacities that takes as its inspiration the investigation of simple systems exhibiting emergent behavior. Rich with examples, the book shows with extraordinary clarity how ideas from embodied cognitive science, robotics, artificial life, and connectionism can be combined to shed new light on the workings of the mind. It's hard to imagine a better book for anyone wishing to understand the latest advances in cognitive science.\" \u003ci\u003eLarry Shapiro, University of Wisconsin\u003c\/i\u003e \u003cbr\u003e \u003cp\u003e\u003cbr\u003e \u003c\/p\u003e \u003cp\u003e\"\u003ci\u003eMinds and Machines\u003c\/i\u003e provides an easily understood introduction to synthetic psychology – start with simple processes, see what emerges, and analyze the resulting system. Dawson lays a solid foundation describing the strengths and weaknesses of various modeling approaches in psychology, and then builds on this by giving concrete examples of how connectionism – using the synthetic approach – can be used to provide simple explanations of seemingly complex cognitive phenomena.\" \u003ci\u003eDavid A. Medler, The Medical College of Wisconsin\u003c\/i\u003e\u003cbr\u003e \u003c\/p\u003e \u003cp\u003e\u003cbr\u003e \u003c\/p\u003e \u003cp\u003e\"Thisis a wonderful book, both in terms of the thought-provoking technical content and the delightfully conversational style that readers have come to expect from the author of \u003ci\u003eUnderstanding Cognitive Science\u003c\/i\u003e. Dawson has a real gift for presenting complex ideas in an accessible and engaging way that does not dilute the scientific or philosophical intricacies involved.\" \u003ci\u003eStefan C. Kremer, University of Guelph, Canada\u003c\/i\u003e\u003cbr\u003e \u003c\/p\u003e \u003cp\u003e\"An important virtue of this book is that the content and order of presentation has clearly been tested at length in the classroom of a dedicated and creative teacher. The book has many illustrations from teaching practice, and would be an excellent basis for a senior undergraduate or introductory graducate course on cognitive modelling, and I'd be delighted to use it for that purpose myself ... This is a fine book, and I suspect it would be a valuable resource for those who don't know much about synthetic psychology but would like to get a clear sense of the lie of the land.\" \u003ci\u003eDavid Spurrett, University of KwaZulu-Natal, Psychology in Society, 30, 2004, 77-79\u003c\/i\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eList of Figures.\u003c\/b\u003e \u003cp\u003e\u003cb\u003eList of Tables.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. The Kids in the Hall.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSynthetic Versus Analytic Traditions. \u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Advantages and Disadvantages of Modeling.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is A Model?.\u003c\/p\u003e \u003cp\u003eAdvantages and Disadvantages of Models. \u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Models of Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAn Example of a Model of Data.\u003c\/p\u003e \u003cp\u003eProperties of Models of Data. \u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Mathematical Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAn Example Mathematical Model.\u003c\/p\u003e \u003cp\u003eMathematical Models vs. Models of Data. \u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Computer Simulations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Sample Computer Simulation.\u003c\/p\u003e \u003cp\u003eConnectionist Models.\u003c\/p\u003e \u003cp\u003eProperties of Computer Simulations. \u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. First Steps Toward Synthetic Psychology.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eBuilding a Thoughtless Walker.\u003c\/p\u003e \u003cp\u003eStep 1: Synthesis.\u003c\/p\u003e \u003cp\u003eStep 2: Emergence.\u003c\/p\u003e \u003cp\u003eStep 3: Analysis.\u003c\/p\u003e \u003cp\u003eIssues Concerning Synthetic Psychology. \u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Uphill Analysis, Downhill Synthesis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eFrom Homeostats to Tortoises.\u003c\/p\u003e \u003cp\u003eAshby’s Homeostat.\u003c\/p\u003e \u003cp\u003eVehicles.\u003c\/p\u003e \u003cp\u003eSynthesis and Emergence: Some Modern Examples.\u003c\/p\u003e \u003cp\u003eThe Law of Uphill Analysis and Downhill Synthesis. \u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Connectionism As Synthetic Psychology.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction.\u003c\/p\u003e \u003cp\u003eBeyond Sensory Reflexes.\u003c\/p\u003e \u003cp\u003eConnectionism, Synthesis, and Representation.\u003c\/p\u003e \u003cp\u003eSummary and Conclusions. \u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Building Associations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFrom Associationism To Connectionism.\u003c\/p\u003e \u003cp\u003eBuilding An Associative Memory.\u003c\/p\u003e \u003cp\u003eBeyond the Limitations of Hebb Learning.\u003c\/p\u003e \u003cp\u003eAssociative Memory and Synthetic Psychology. \u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Making Decisions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Limits of Linearity.\u003c\/p\u003e \u003cp\u003eA Fundamental Nonlinearity.\u003c\/p\u003e \u003cp\u003eBuilding a Perceptron: A Nonlinear Associative Memory.\u003c\/p\u003e \u003cp\u003eThe Psychology of Perceptrons.\u003c\/p\u003e \u003cp\u003eThe Need for Layers. \u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Sequences of Decisions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Logic of Layers.\u003c\/p\u003e \u003cp\u003eTraining Multilayered Networks.\u003c\/p\u003e \u003cp\u003eA Simple Case Study: Exclusive Or.\u003c\/p\u003e \u003cp\u003eA Second Case Study: Classifying Musical Chords.\u003c\/p\u003e \u003cp\u003eA Third Case Study: From Connectionism to Selectionism. \u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. From Synthesis To Analysis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRepresenting Musical Chords in a Pdp Network.\u003c\/p\u003e \u003cp\u003eInterpreting the Internal Structure of Value Unit Networks.\u003c\/p\u003e \u003cp\u003eNetwork Interpretation and Synthetic Psychology. \u003cb\u003e.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. From Here To Synthetic Psychology.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex\u003c\/p\u003e","brand":"John Wiley and Sons Ltd","offers":[{"title":"Default Title","offer_id":49407848087895,"sku":"9781405113489","price":99.86,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781405113489.jpg?v=1730500729"},{"product_id":"nonlinear-economic-models-cross-sectional-time-series-and-neural-network-applications-9781858986371","title":"Nonlinear Economic Models: Cross-sectional, Time","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eNonlinear modelling has become increasingly important and widely used in economics.  This valuable book brings together recent advances in the area including contributions covering cross-sectional studies of income distribution and discrete choice models, time series models of exchange rate dynamics and jump processes, and artificial neural network and genetic algorithm models of financial markets.  Attention is given to the development of theoretical models as well as estimation and testing methods with a wide range of applications in micro and macroeconomics, labour and finance.\u003cp\u003eThe book provides valuable introductory material that is accessible to students and scholars interested in this exciting research area, as well as presenting the results of new and original research.  \u003ci\u003eNonlinear Economic Models\u003c\/i\u003e provides a sequel to \u003ci\u003eChaos and Nonlinear Models in Economics\u003c\/i\u003e by the same editors.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003ci\u003e'This collection provides valuable introductory material that is accessible to students and scholars interested in this research area.'\u003c\/i\u003e -- Business Horizons\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eContents:  Part I:  Introduction  1. Nonlinear Modelling: An Introduction  Part II:  Cross-sectional Applications 2. A Model of Income Distribution  3. Truncated Distribution Families  4. Betit: A Flexible Binary Choice Model  5. Estimation of Generalised Distributions  6. Age and the Distribution of Earnings  7. Count Data and Discrete Distributions  Part III:  Time Series Applications  8. A Model of the Real Exchange Rate  9. Jump Models and Higher Moments  10. A Topological Test of Chaos  11. Genetic Algorithms and Trading Rules  Part IV:  Neural Network Applications  12. Artificial Neural Networks  13. An ANN Model of the Stock Market  14. Exchange Rate Forecasting Models  Index","brand":"Edward Elgar Publishing Ltd","offers":[{"title":"Default Title","offer_id":49414058901847,"sku":"9781858986371","price":111.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781858986371.jpg?v=1730522311"},{"product_id":"proceedings-of-the-22nd-engineering-applications-of-neural-networks-conference-eann-2021-9783030805678","title":"Proceedings of the 22nd Engineering Applications","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book contains the proceedings of the 22nd EANN “Engineering Applications of Neural Networks” 2021 that comprise of research papers on both theoretical foundations and cutting-edge applications of artificial intelligence. Based on the discussed research areas, emphasis is given in advances of machine learning (ML) focusing on the following algorithms-approaches: Augmented ML, autoencoders, adversarial neural networks, blockchain-adaptive methods, convolutional neural networks, deep learning, ensemble methods, learning-federated learning, neural networks, recurrent – long short-term memory. The application domains are related to: Anomaly detection, bio-medical AI, cyber-security, data fusion, e-learning, emotion recognition, environment, hyperspectral imaging, fraud detection, image analysis, inverse kinematics, machine vision, natural language, recommendation systems, robotics, sentiment analysis, simulation, stock market prediction.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eAutomatic Facial Expression Neutralisation Using Generative Adversarial Network.- Creating Ensembles of Generative Adversarial Network Discriminators for One-class Classification.- A Hybrid Deep Learning Ensemble for Cyber Intrusion Detection.- Anomaly Detection by Robust Feature Reconstruction.- Deep Learning of Brain Asymmetry Images and Transfer Learning for Early Diagnosis of Dementia.- Deep learning topology-preserving EEG-based images for autism detection in infants.- Improving the Diagnosis of Breast Cancer by Combining Visual and Semantic Feature Descriptors.- Liver cancer trait detection and classification through Machine Learning on smart mobile devices.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415642874199,"sku":"9783030805678","price":224.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030805678.jpg?v=1730527625"},{"product_id":"fuzzy-information-processing-2020-proceedings-of-the-2020-annual-conference-of-the-north-american-fuzzy-information-processing-society-nafips-2020-9783030815608","title":"Fuzzy Information Processing 2020: Proceedings of","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book describes how to use expert knowledge—which is often formulated by using imprecise (fuzzy) words from a natural language. In the 1960s, Zadeh designed special \"fuzzy\" techniques for such use. In the 1980s, fuzzy techniques started controlling trains, elevators, video cameras, rice cookers, car transmissions, etc. Now, combining fuzzy with neural, genetic, and other intelligent methods leads to new state-of-the-art results: in aerospace industry (from drones to space flights), in mobile robotics, in finances (predicting the value of crypto-currencies), and even in law enforcement (detecting counterfeit banknotes, detecting online child predators and in creating explainable AI systems). The book describes these (and other) applications—as well as foundations and logistics of fuzzy techniques. This book can be recommended to specialists—both in fuzzy and in various application areas—who will learn latest techniques and their applications, and to students interested in innovative ideas.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePowerset operators in categories with fuzzy relations dened by monads.- Improved Fuzzy Q-Learning with Replay Memory.- The ulem package: underlining for emphasis.- A Dynamic Hierarchical Genetic-Fuzzy Sugeno Network.- Fuzzy Mathematical Morphology and Applications in Image Processing.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":49415643988311,"sku":"9783030815608","price":179.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030815608.jpg?v=1730527628"},{"product_id":"neural-information-processing-29th-international-conference-iconip-2022-virtual-event-november-22-26-2022-proceedings-part-i-9783031301049","title":"Neural Information Processing: 29th International","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022.\u003cbr\u003eThe 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications.\u003cbr\u003eThe ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eTheory and Algorithms.- \u003c\/b\u003eSolving Partial Differential Equations using Point-based Neural Networks.- Patch Mix Augmentation with Dual Encoders for Meta-Learning.- Tacit Commitments Emergence in Multi-agent Reinforcement Learning.- Saccade Direction Information Channel.- Shared-Attribute Multi-Graph Clustering with Global Self-Attention.- Mutual Diverse-Label Adversarial Training.-  Multi-Agent Hyper-Attention Policy Optimization.-  Filter Pruning via Similarity Clustering for Deep Convolutional Neural Networks.- FPD: Feature Pyramid Knowledge Distillation.- An effective ensemble model related to incremental learning in neural machine translation.- Local-Global Semantic Fusion Single-shot Classification Method.- Self-Reinforcing Feedback Domain Adaptation Channel.- General Algorithm for Learning from Grouped Uncoupled Data and Pairwise Comparison Data.- Additional Learning for Joint Probability Distribution Matching in BiGAN.- Multi-View Self-Attention for Regression Domain Adaptation with Feature Selection.- EigenGRF: Layer-Wise Eigen-Learning for Controllable Generative Radiance Fields.- Partial Label learning with Gradually Induced Error-Correction Output Codes.- HMC-PSO: A Hamiltonian Monte Carlo and Particle Swarm Optimization-based optimizer.-  Heterogeneous Graph Representation for Knowledge Tracing.- Intuitionistic fuzzy universum support vector machine.- Support vector machine based models with sparse auto-encoder based features for classification problem.- Selectively increasing the diversity of GAN-generated samples.- Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning.- Differentiable Causal Discovery Under Heteroscedastic Noise.- IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels.- Adaptive Scaling for U-Net in Time Series Classification.- Permutation Elementary Cellular Automata:  Analysis and Application of Simple Examples.- SSPR: A Skyline-Based Semantic Place Retrieval Method.- Double Regularization-based RVFL and edRVFL Networks for Sparse-Dataset Classification.- Adaptive Tabu Dropout for Regularization of Deep Neural Networks.- Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action Localization.- Nearest Neighbor Classifier with Margin Penalty for Active Learning.- Factual Error Correction in Summarization with Retriever-Reader Pipeline.- Context-adapted Multi-policy Ensemble Method for Generalization in Reinforcement Learning.- Self-attention based multi-scale graph convolutional networks.- Synesthesia Transformer with Contrastive Multimodal Learning.- Context-based Point Generation Network for Point Cloud Completion.- Temporal Neighborhood Change Centrality for Important Node Identification in Temporal Networks.- DOM2R-Graph: A Web Attribute Extraction Architecture with Relation-aware Heterogeneous Graph Transformer.- Sparse Linear Capsules for Matrix Factorization-based Collaborative Filtering.- PromptFusion: a Low-cost Prompt-based Task Composition for Multi-task Learning.-  A fast and efficient algorithm for filtering the training dataset.- Entropy-minimization Mean Teacher for Source-Free Domain Adaptive Object Detection.- IA-CL: A Deep Bidirectional Competitive Learning Method for Traveling Salesman Problem.- Boosting Graph Convolutional Networks With Semi-Supervised Training.- Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs.- VAAC: V-value Attention Actor-Critic for Cooperative Multi-agent Reinforcement Learning.- An Analytical Estimation of Spiking Neural Networks Energy Efficiency.- Correlation Based Semantic Transfer with Application to Domain Adaptation.- Minimum Variance Embedded Intuitionistic Fuzzy Weighted Random Vector Functional Link Network.- Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":49415706607959,"sku":"9783031301049","price":75.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031301049.jpg?v=1730527854"},{"product_id":"hesitant-fuzzy-set-theory-and-extension-9789811673009","title":"Hesitant Fuzzy Set: Theory and Extension","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eCovering a wide range of notions concerning hesitant fuzzy set and its extensions, this book provides a comprehensive reference to the topic. In the case where different sources of vagueness appear simultaneously, the concept of fuzzy set is not able to properly model the uncertainty, imprecise and vague information. In order to overcome such a limitation, different types of fuzzy extension have been introduced so far. Among them, hesitant fuzzy set was first introduced in 2010, and the existing extensions of hesitant fuzzy set have been encountering an increasing interest and attracting more and more attentions up to now. It is not an exaggeration to say that the recent decade has seen the blossoming of a larger set of techniques and theoretical outcomes for hesitant fuzzy set together with its extensions as well as applications.As the research has moved beyond its infancy, and now it is entering a maturing phase with increased numbers and types of extensions, this book aims to give a comprehensive review of such researches. Presenting the review of many and important types of hesitant fuzzy extensions, and including references to a large number of related publications, this book will serve as a useful reference book for researchers in this field.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1: Hesitant Fuzzy Set.- Chapter 2: Hesitant Fuzzy Linguistic Term Set.- Chapter 3: Neutrosophic Hesitant Fuzzy Set.- Chapter 4: Pythagorean Hesitant Fuzzy Set.- Chapter 5: q-Rung Orthopair Hesitant Fuzzy Set.- Chapter 6: Probabilistic Hesitant Fuzzy Set.- Chapter 7: Type 2 Hesitant Fuzzy Set.- Chapter 8: Hesitant Bipolar Fuzzy Set.- Chapter 9: Cubic Hesitant Fuzzy Set.- Chapter 10: Complex Hesitant Fuzzy Set.- Chapter 11: Picture Hesitant Fuzzy Set.- Chapter 12: Spherical Hesitant Fuzzy Set.","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":49427834208599,"sku":"9789811673009","price":98.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811673009.jpg?v=1730565835"},{"product_id":"neural-information-processing-29th-international-conference-iconip-2022-virtual-event-november-22-26-2022-proceedings-part-iv-9789819916382","title":"Neural Information Processing: 29th International","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe four-volume set CCIS 1791, 1792, 1793 and 1794 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022.  \u003cbr\u003eThe 213 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications.\u003cbr\u003eThe ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e​Theory and Algorithms.- \u003c\/b\u003eKnowledge Transfer from Situation Evaluation to Multi-agent Reinforcement Learning.- Sequential three-way rules class-overlap under-sampling based on fuzzy hierarchical subspace for imbalanced data.- Two-stage Multilayer Perceptron Hawkes Process.- The Context Hierarchical Contrastive Learning for Time Series in Frequency Domain.- Hawkes Process via Graph Contrastive Discriminant representation Learning and Transformer capturing long-term dependencies.- A Temporal Consistency Enhancement Algorithm Based On Pixel Flicker Correction.-  Data representation and clustering with double low-rank constraints.- RoMA: a Method for Neural Network Robustness Measurement and Assessment.- Independent Relationship Detection for Real-Time Scene Graph Generation.- A multi-label feature selection method based on feature graph with ridge regression and eigenvector centrality.- O3GPT: A Guidance-Oriented Periodic Testing Framework with Online Learning, Online Testing, and Online Feedback.- AFFSRN: Attention-Based Feature Fusion Super-Resolution Network.- Temporal-Sequential Learning with Columnar-Structured Spiking Neural Networks.- Graph Attention Transformer Network for Robust Visual Tracking.- GCL-KGE:Graph Contrastive Learning for Knowledge Graph Embedding.- Towards a Unified Benchmark for Reinforcement Learning in Sparse Reward Environments.- Effect of Logistic Activation Function and Multiplicative Input Noise on DNN-kWTA model.- A High-Speed SSVEP-Based Speller Using Continuous Spelling Method.- AAT: Non-Local Networks for Sim-to-Real Adversarial Augmentation Transfer.- Aggregating Intra-class and Inter-class information for Multi-label Text Classification.- Fast estimation of multidimensional regression functions by the Parzen kernel-based method.- ReGAE: Graph autoencoder based on recursive neural networks.- Efficient Uncertainty Quantification for Under-constraint Prediction following Learning using MCMC.- SMART: A Robustness Evaluation Framework for Neural Networks.- Time-aware Quaternion Convolutional Network for Temporal Knowledge Graph Reasoning.- SumBART - An improved BART model for abstractive text summarization.- Saliency-Guided Learned Image Compression for Object Detection.- Multi-Label Learning with Data Self-Augmentation.- MnRec: A News Recommendation Fusion Model Combining Multi-granularity Information.- Infinite Label Selection Method for Mutil-label Classification.- Simultaneous Perturbation Method for Multi-Task Weight Optimization in One-Shot Meta-Learning.- Searching for Textual Adversarial Examples with Learned Strategy.- Multivariate Time Series Retrieval with Binary Coding from Transformer. -Learning TSP Combinatorial Search and Optimization with Heuristic Search.- A Joint Learning Model for Open Set Recognition with Post-processing.- Cross-Layer Fusion for Feature Distillation.- MCHPT: A Weakly Supervise Based Merchant Pre-trained Model.- Progressive Latent Replay for efficient Generative Rehearsal.- Generalization Bounds for Set-to-Set Matching with Negative Sampling.- ADA: An Attention-Based Data Augmentation Approach to Handle Imbalanced Textual Datasets.- Countering the Anti-detection Adversarial Attacks.- Evolving Temporal Knowledge Graphs by Iterative Spatio-Temporal Walks.- Improving Knowledge Graph Embedding Using Dynamic Aggregation of Neighbor Information.- Generative Generalized Zero-Shot Learning based on Auxiliary-Features.- Learning Stable Representations with Progressive Autoencoder (PAE).- Effect of Image Down-sampling on Detection of Adversarial Examples .- Boosting the Robustness of Neural Networks with M-PGD.- StatMix: Data augmentation method that relies on image statistics in federated learning.- Classification by Components Including Chow's Reject Option. -Community discovery algorithm based on improved deep sparse autoencoder.- Fairly Constricted Multi-Objective Particle Swarm Optimization.- Argument Classification with BERT plus Contextual, Structural and Syntactic Features as Text.- Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient.- Optimizing Knowledge Distillation Via Shallow Texture Knowledge Transfer.- Unsupervised Domain Adaptation Supplemented with Generated Images.- MAR2MIX: A Novel Model for Dynamic Problem in Multi-Agent Reinforcement Learning.- Adversarial Training with Knowledge Distillation Considering Intermediate Representations in CNNs.- Deep Contrastive Multi-view Subspace Clustering.","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":49427883622743,"sku":"9789819916382","price":85.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789819916382.jpg?v=1730565994"},{"product_id":"neural-information-processing-30th-international-conference-iconip-2023-changsha-china-november-20-23-2023-proceedings-part-v-9789819980727","title":"Neural Information Processing: 30th International","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. \u003cbr\u003eThe 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields. \u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eText to Image Generation with Conformer-GAN.- MGFNet: A Multi-Granularity Feature Fusion and Mining Network for Visible-Infrared Person Re-Identification.- Isomorphic Dual-Branch Network for Non-homogeneous Image Dehazing and Super-Resolution.- Hi-Stega : A Hierarchical Linguistic Steganography Framework Combining Retrieval and Generation.- Effi-Seg: Rethinking EfficientNet Architecture for Real-time Semantic Segmentation.- Quantum Autoencoder Frameworks for Network Anomaly Detection.- Spatially-Aware Human-Object Interaction Detection with Cross-Modal Enhancement.-  Intelligent trajectory tracking control of unmanned parafoil system based on SAC optimized LADRC.- CATS: Connection-aware and Interaction-based Text Steganalysis in Social Networks.- Syntax Tree Constrained Graph Network for Visual Question Answering.- CKR-Calibrator: Convolution Kernel Robustness Evaluation and Calibration.- SGLP-Net: Sparse Graph Label Propagation Network for Weakly-Supervised Temporal Action Localization.- VFIQ: A Novel Model of ViT-FSIMc Hybrid Siamese Network for Image Quality Assessment.- Spiking Reinforcement Learning for Weakly-supervised Anomaly Detection.- Resource-aware DNN Partitioning for Privacy-sensitive Edge-Cloud Systems.- A frequency reconfigurable multi-mode printed antenna.-  Multi-view Contrastive learning for Knowledge-aware Recommendation.- PYGC: a PinYin Language Model Guided Correction Model for Chinese Spell Checking.- Empirical Analysis of Multi-label Classification on GitterCom using BERT.- A lightweight safety helmet detection network based on bidirectional connection module and Polarized Self-Attention.- Direct Inter-Intra View Association for Light Field Super-Resolution.- Responsive CPG-Based Locomotion Control for Quadruped Robots.- Vessel Behavior Anomaly Detection using Graph Attention Network.- TASFormer: Task-aware Image Segmentation Transformer.-  Unsupervised Joint-Semantics Autoencoder Hashing for Multimedia Retrieval.- TKGR-RHETNE：A New Temporal Knowledge Graph Reasoning Model via Jointly Modeling Relevant Historical Event and Temporal Neighborhood Event Context.- High-Resolution Self-Attention with Fair Loss for Point Cloud Segmentation.- Transformer-based Video Deinterlacing Method.- SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack.- CSEC: A Chinese Semantic Error Correction Dataset for Written Correction.- Contrastive Kernel Subspace Clustering.- UATR: An Uncertainty Aware Two-stage Refinement Model for Targeted Sentiment Analysis.- AttIN: Paying More Attention to Neighborhood Information for Entity Typing in Knowledge Graphs.- Text-based Person Re-ID by Saliency Mask and Dynamic Label Smoothing.- Robust Multi-view Spectral Clustering with Auto-encoder for Preserving Information.-  Learnable Color Image Zero-Watermarking Based on Feature Comparison.-  P-IoU: Accurate Motion Prediction based Data Association for Multi-Object Tracking.- WCA-VFnet:a dedicated complex forest smoke fire detector.- Label Selection Algorithm Based on Ant Colony Optimization and Reinforcement Learning for Multi-label Classification.- Reversible Data Hiding Based on Adaptive Embedding with Local Complexity.- Generalized Category Discovery with Clustering Assignment Consistency.- CInvISP: Conditional Invertible Image Signal Processing Pipeline.- Ignored Details in Eyes: Exposing GAN-generated Faces by Sclera.- A Developer Recommendation Method Based on Disentangled.- Graph Convolutional Network.- Novel Method for Radar Echo Target Detection.","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":49427887227223,"sku":"9789819980727","price":66.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789819980727.jpg?v=1730566008"},{"product_id":"ai-for-cars-9780367565190","title":"AI for Cars","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eArtificial Intelligence (AI) is undoubtedly playing an increasingly significant role in automobile technology. In fact, cars inhabit one of just a few domains where you will find many AI innovations packed into a single product.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eAI for Cars\u003c\/i\u003e provides a brief guided tour through many different AI landscapes including robotics, image and speech processing, recommender systems and onto deep learning, all within the automobile world. From pedestrian detection to driver monitoring to recommendation engines, the book discusses the background, research and progress thousands of talented engineers and researchers have achieved thus far, and their plans to deploy this life-saving technology all over the world.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForeword \u003c\/p\u003e\u003cp\u003ePreface \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAI for Advanced Driver Assistance Systems \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eAutomatic Parking \u003c\/p\u003e\u003cp\u003eTraffic Sign Recognition \u003c\/p\u003e\u003cp\u003eDriver Monitoring System \u003c\/p\u003e\u003cp\u003eSummary \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAI for Autonomous Driving \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003ePerception \u003c\/p\u003e\u003cp\u003ePlanning \u003c\/p\u003e\u003cp\u003eMotion Control \u003c\/p\u003e\u003cp\u003eSummary \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAI for In-Vehicle Infotainment Systems \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eGesture Control \u003c\/p\u003e\u003cp\u003eVoice Assistant \u003c\/p\u003e\u003cp\u003eUser Action Prediction \u003c\/p\u003e\u003cp\u003eSummary \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAI for Research \u0026amp; Development \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eAutomated Rules Generation \u003c\/p\u003e\u003cp\u003eVirtual Testing Platform \u003c\/p\u003e\u003cp\u003eSynthetic Scenario Generation \u003c\/p\u003e\u003cp\u003eSummary \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAI for Services \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003ePredictive Diagnostics \u003c\/p\u003e\u003cp\u003ePredictive Maintenance \u003c\/p\u003e\u003cp\u003eDriver Behavior Analysis \u003c\/p\u003e\u003cp\u003eSummary \u003c\/p\u003e\u003cp\u003e\u003cb\u003eThe Future of AI in Cars \u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eA Tale Of Two Paradigms \u003c\/p\u003e\u003cp\u003eAI \u0026amp; Car Safety \u003c\/p\u003e\u003cp\u003eAI \u0026amp; Car Security \u003c\/p\u003e\u003cp\u003eSummary \u003c\/p\u003e\u003cp\u003eFurther Reading \u003c\/p\u003e\u003cp\u003eReferences \u003c\/p\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":49896804647255,"sku":"9780367565190","price":22.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"fuzzy-computing-in-data-science-9781119864929","title":"Fuzzy Computing in Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eFUZZY COMPUTING IN DATA SCIENCE\u003c\/b\u003e \u003cp\u003e\u003cb\u003eThis book comprehensively explains how to use various fuzzy-based models to solve real-time industrial challenges.\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThe book provides information about fundamental aspects of the field and explores the myriad applications of fuzzy logic techniques and methods. It presents basic conceptual considerations and case studies of applications of fuzzy computation. It covers the fundamental concepts and techniques for system modeling, information processing, intelligent system design, decision analysis, statistical analysis, pattern recognition, automated learning, system control, and identification. The book also discusses the combination of fuzzy computation techniques with other computational intelligence approaches such as neural and evolutionary computation. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eResearchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003eAcknowledgement xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Band Reduction of HSI Segmentation Using FCM 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eV. Saravana Kumar, S. Anantha Sivaprakasam, E.R. Naganathan, Sunil Bhutada and M. Kavitha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Existing Method 3\u003c\/p\u003e \u003cp\u003e1.2.1 K-Means Clustering Method 3\u003c\/p\u003e \u003cp\u003e1.2.2 Fuzzy C-Means 3\u003c\/p\u003e \u003cp\u003e1.2.3 Davies Bouldin Index 4\u003c\/p\u003e \u003cp\u003e1.2.4 Data Set Description of HSI 4\u003c\/p\u003e \u003cp\u003e1.3 Proposed Method 5\u003c\/p\u003e \u003cp\u003e1.3.1 Hyperspectral Image Segmentation Using Enhanced Estimation of Centroid 5\u003c\/p\u003e \u003cp\u003e1.3.2 Band Reduction Using K-Means Algorithm 6\u003c\/p\u003e \u003cp\u003e1.3.3 Band Reduction Using Fuzzy C-Means 7\u003c\/p\u003e \u003cp\u003e1.4 Experimental Results 8\u003c\/p\u003e \u003cp\u003e1.4.1 DB Index Graph 8\u003c\/p\u003e \u003cp\u003e1.4.2 K-Means–Based PSC (EEOC) 9\u003c\/p\u003e \u003cp\u003e1.4.3 Fuzzy C-Means–Based PSC (EEOC) 10\u003c\/p\u003e \u003cp\u003e1.5 Analysis of Results 12\u003c\/p\u003e \u003cp\u003e1.6 Conclusions 16\u003c\/p\u003e \u003cp\u003eReferences 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 A Fuzzy Approach to Face Mask Detection 21\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVatsal Mishra, Tavish Awasthi, Subham Kashyap, Minerva Brahma, Monideepa Roy and Sujoy Datta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 22\u003c\/p\u003e \u003cp\u003e2.2 Existing Work 23\u003c\/p\u003e \u003cp\u003e2.3 The Proposed Framework 26\u003c\/p\u003e \u003cp\u003e2.4 Set-Up and Libraries Used 26\u003c\/p\u003e \u003cp\u003e2.5 Implementation 27\u003c\/p\u003e \u003cp\u003e2.6 Results and Analysis 29\u003c\/p\u003e \u003cp\u003e2.7 Conclusion and Future Work 33\u003c\/p\u003e \u003cp\u003eReferences 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Application of Fuzzy Logic to the Healthcare Industry 37\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBiswajeet Sahu, Lokanath Sarangi, Abhinadita Ghosh and Hemanta Kumar Palo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 38\u003c\/p\u003e \u003cp\u003e3.2 Background 41\u003c\/p\u003e \u003cp\u003e3.3 Fuzzy Logic 42\u003c\/p\u003e \u003cp\u003e3.4 Fuzzy Logic in Healthcare 45\u003c\/p\u003e \u003cp\u003e3.5 Conclusions 49\u003c\/p\u003e \u003cp\u003eReferences 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 A Bibliometric Approach and Systematic Exploration of Global Research Activity on Fuzzy Logic in Scopus Database 55\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSugyanta Priyadarshini and Nisrutha Dulla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 56\u003c\/p\u003e \u003cp\u003e4.2 Data Extraction and Interpretation 58\u003c\/p\u003e \u003cp\u003e4.3 Results and Discussion 59\u003c\/p\u003e \u003cp\u003e4.3.1 Per Year Publication and Citation Count 59\u003c\/p\u003e \u003cp\u003e4.3.2 Prominent Affiliations Contributing Toward Fuzzy Logic 60\u003c\/p\u003e \u003cp\u003e4.3.3 Top Journals Emerging in Fuzzy Logic in Major Subject Areas 61\u003c\/p\u003e \u003cp\u003e4.3.4 Major Contributing Countries Toward Fuzzy Research Articles 63\u003c\/p\u003e \u003cp\u003e4.3.5 Prominent Authors Contribution Toward the Fuzzy Logic Analysis 66\u003c\/p\u003e \u003cp\u003e4.3.6 Coauthorship of Authors 67\u003c\/p\u003e \u003cp\u003e4.3.7 Cocitation Analysis of Cited Authors 68\u003c\/p\u003e \u003cp\u003e4.3.8 Cooccurrence of Author Keywords 68\u003c\/p\u003e \u003cp\u003e4.4 Bibliographic Coupling of Documents, Sources, Authors, and Countries 70\u003c\/p\u003e \u003cp\u003e4.4.1 Bibliographic Coupling of Documents 70\u003c\/p\u003e \u003cp\u003e4.4.2 Bibliographic Coupling of Sources 71\u003c\/p\u003e \u003cp\u003e4.4.3 Bibliographic Coupling of Authors 72\u003c\/p\u003e \u003cp\u003e4.4.4 Bibliographic Coupling of Countries 73\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 74\u003c\/p\u003e \u003cp\u003eReferences 76\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Fuzzy Decision Making in Predictive Analytics and Resource Scheduling 79\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRekha A. Kulkarni, Suhas H. Patil and Bithika Bishesh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 80\u003c\/p\u003e \u003cp\u003e5.2 History of Fuzzy Logic and Its Applications 81\u003c\/p\u003e \u003cp\u003e5.3 Approximate Reasoning 82\u003c\/p\u003e \u003cp\u003e5.4 Fuzzy Sets vs Classical Sets 83\u003c\/p\u003e \u003cp\u003e5.5 Fuzzy Inference System 84\u003c\/p\u003e \u003cp\u003e5.5.1 Characteristics of FIS 85\u003c\/p\u003e \u003cp\u003e5.5.2 Working of FIS 85\u003c\/p\u003e \u003cp\u003e5.5.3 Methods of FIS 86\u003c\/p\u003e \u003cp\u003e5.6 Fuzzy Decision Trees 86\u003c\/p\u003e \u003cp\u003e5.6.1 Characteristics of Decision Trees 87\u003c\/p\u003e \u003cp\u003e5.6.2 Construction of Fuzzy Decision Trees 87\u003c\/p\u003e \u003cp\u003e5.7 Fuzzy Logic as Applied to Resource Scheduling in a Cloud Environment 88\u003c\/p\u003e \u003cp\u003e5.8 Conclusion 90\u003c\/p\u003e \u003cp\u003eReferences 91\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model 93\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. Mala and V. Umadevi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 94\u003c\/p\u003e \u003cp\u003e6.1.1 Aim and Scope 94\u003c\/p\u003e \u003cp\u003e6.1.2 R-Tool 94\u003c\/p\u003e \u003cp\u003e6.1.3 Application of Fuzzy Logic 94\u003c\/p\u003e \u003cp\u003e6.1.4 Dataset 95\u003c\/p\u003e \u003cp\u003e6.2 Model Study 96\u003c\/p\u003e \u003cp\u003e6.2.1 Introduction to Machine Learning Method 96\u003c\/p\u003e \u003cp\u003e6.2.2 Time Series Analysis 96\u003c\/p\u003e \u003cp\u003e6.2.3 Components of a Time Series 97\u003c\/p\u003e \u003cp\u003e6.2.4 Concepts of Stationary 99\u003c\/p\u003e \u003cp\u003e6.2.5 Model Parsimony 100\u003c\/p\u003e \u003cp\u003e6.3 Methodology 100\u003c\/p\u003e \u003cp\u003e6.3.1 Exploratory Data Analysis 100\u003c\/p\u003e \u003cp\u003e6.3.1.1 Seed Types—Analysis 101\u003c\/p\u003e \u003cp\u003e6.3.1.2 Comparison of Location and Seeds 101\u003c\/p\u003e \u003cp\u003e6.3.1.3 Comparison of Season (Month) and Seeds 103\u003c\/p\u003e \u003cp\u003e6.3.2 Forecasting 103\u003c\/p\u003e \u003cp\u003e6.3.2.1 Auto Regressive Integrated Moving Average (ARIMA) 103\u003c\/p\u003e \u003cp\u003e6.3.2.2 Data Visualization 106\u003c\/p\u003e \u003cp\u003e6.3.2.3 Implementation Model 108\u003c\/p\u003e \u003cp\u003e6.4 Result Analysis 108\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 110\u003c\/p\u003e \u003cp\u003eReferences 110\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Modified m-Polar Fuzzy Set ELECTRE-I Approach 113\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMadan Jagtap, Prasad Karande and Pravin Patil\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 114\u003c\/p\u003e \u003cp\u003e7.1.1 Objectives 114\u003c\/p\u003e \u003cp\u003e7.2 Implementation of m-Polar Fuzzy ELECTRE-I Integrated Shannon’s Entropy Weight Calculations 115\u003c\/p\u003e \u003cp\u003e7.2.1 The m-Polar Fuzzy ELECTRE-I Integrated Shannon’s Entropy Weight Calculation Method 115\u003c\/p\u003e \u003cp\u003e7.3 Application to Industrial Problems 118\u003c\/p\u003e \u003cp\u003e7.3.1 Cutting Fluid Selection Problem 118\u003c\/p\u003e \u003cp\u003e7.3.2 Results Obtained From m-Polar Fuzzy ELECTRE-I for Cutting Fluid Selection Problem 122\u003c\/p\u003e \u003cp\u003e7.3.3 FMS Selection Problem 125\u003c\/p\u003e \u003cp\u003e7.3.4 Results Obtained From m-Polar Fuzzy ELECTRE-I for FMS Selection 130\u003c\/p\u003e \u003cp\u003e7.4 Conclusions 143\u003c\/p\u003e \u003cp\u003eReferences 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Fuzzy Decision Making: Concept and Models 147\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBithika Bishesh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 148\u003c\/p\u003e \u003cp\u003e8.2 Classical Set 149\u003c\/p\u003e \u003cp\u003e8.3 Fuzzy Set 150\u003c\/p\u003e \u003cp\u003e8.4 Properties of Fuzzy Set 151\u003c\/p\u003e \u003cp\u003e8.5 Types of Decision Making 153\u003c\/p\u003e \u003cp\u003e8.5.1 Individual Decision Making 153\u003c\/p\u003e \u003cp\u003e8.5.2 Multiperson Decision Making 157\u003c\/p\u003e \u003cp\u003e8.5.3 Multistage Decision Making 158\u003c\/p\u003e \u003cp\u003e8.5.4 Multicriteria Decision Making 160\u003c\/p\u003e \u003cp\u003e8.6 Methods of Multiattribute Decision Making (MADM) 162\u003c\/p\u003e \u003cp\u003e8.6.1 Weighted Sum Method (WSM) 162\u003c\/p\u003e \u003cp\u003e8.6.2 Weighted Product Method (WPM) 162\u003c\/p\u003e \u003cp\u003e8.6.3 Weighted Aggregates Sum Product Assessment (WASPAS) 163\u003c\/p\u003e \u003cp\u003e8.6.4 Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) 166\u003c\/p\u003e \u003cp\u003e8.7 Applications of Fuzzy Logic 167\u003c\/p\u003e \u003cp\u003e8.8 Conclusion 169\u003c\/p\u003e \u003cp\u003eReferences 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India) 173\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSanjaya Kumar Sahoo and Sukanta Chandra Swain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 174\u003c\/p\u003e \u003cp\u003e9.2 Objectives and Methodology 175\u003c\/p\u003e \u003cp\u003e9.2.1 Objectives 175\u003c\/p\u003e \u003cp\u003e9.2.2 Methodology 176\u003c\/p\u003e \u003cp\u003e9.3 Effect of COVID-19 on the Psychology and Emotion of Repatriated Migrants 176\u003c\/p\u003e \u003cp\u003e9.3.1 Psychological Variables Identified 176\u003c\/p\u003e \u003cp\u003e9.3.2 Fuzzy Logic for Solace to Migrants 176\u003c\/p\u003e \u003cp\u003e9.4 Findings 178\u003c\/p\u003e \u003cp\u003e9.5 Way Out for Strengthening the Psychological Strength of the Migrant Workers through Technological Aid 178\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 179\u003c\/p\u003e \u003cp\u003eReferences 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Fuzzy-Based Edge AI Approach: Smart Transformation of Healthcare for a Better Tomorrow 181\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eB. RaviKrishna, Sirisha Potluri, J. Rethna Virgil Jeny, Guna Sekhar Sajja and Katta Subba Rao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Significance of Machine Learning in Healthcare 182\u003c\/p\u003e \u003cp\u003e10.2 Cloud-Based Artificial Intelligent Secure Models 183\u003c\/p\u003e \u003cp\u003e10.3 Applications and Usage of Machine Learning in Healthcare 183\u003c\/p\u003e \u003cp\u003e10.3.1 Detecting Diseases and Diagnosis 183\u003c\/p\u003e \u003cp\u003e10.3.2 Drug Detection and Manufacturing 183\u003c\/p\u003e \u003cp\u003e10.3.3 Medical Imaging Analysis and Diagnosis 184\u003c\/p\u003e \u003cp\u003e10.3.4 Personalized\/Adapted Medicine 185\u003c\/p\u003e \u003cp\u003e10.3.5 Behavioral Modification 185\u003c\/p\u003e \u003cp\u003e10.3.6 Maintenance of Smart Health Data 185\u003c\/p\u003e \u003cp\u003e10.3.7 Clinical Trial and Study 185\u003c\/p\u003e \u003cp\u003e10.3.8 Crowdsourced Information Discovery 185\u003c\/p\u003e \u003cp\u003e10.3.9 Enhanced Radiotherapy 186\u003c\/p\u003e \u003cp\u003e10.3.10 Outbreak\/Epidemic Prediction 186\u003c\/p\u003e \u003cp\u003e10.4 Edge AI: For Smart Transformation of Healthcare 186\u003c\/p\u003e \u003cp\u003e10.4.1 Role of Edge in Reshaping Healthcare 186\u003c\/p\u003e \u003cp\u003e10.4.2 How AI Powers the Edge 187\u003c\/p\u003e \u003cp\u003e10.5 Edge AI-Modernizing Human Machine Interface 188\u003c\/p\u003e \u003cp\u003e10.5.1 Rural Medicine 188\u003c\/p\u003e \u003cp\u003e10.5.2 Autonomous Monitoring of Hospital Rooms—A Case Study 188\u003c\/p\u003e \u003cp\u003e10.6 Significance of Fuzzy in Healthcare 189\u003c\/p\u003e \u003cp\u003e10.6.1 Fuzzy Logic—Outline 189\u003c\/p\u003e \u003cp\u003e10.6.2 Fuzzy Logic-Based Smart Healthcare 190\u003c\/p\u003e \u003cp\u003e10.6.3 Medical Diagnosis Using Fuzzy Logic for Decision Support Systems 191\u003c\/p\u003e \u003cp\u003e10.6.4 Applications of Fuzzy Logic in Healthcare 193\u003c\/p\u003e \u003cp\u003e10.7 Conclusion and Discussions 193\u003c\/p\u003e \u003cp\u003eReferences 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS 197\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRekha Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 197\u003c\/p\u003e \u003cp\u003e11.2 Video Conferencing Software and Its Major Features 199\u003c\/p\u003e \u003cp\u003e11.2.1 Video Conferencing\/Meeting Software (VC\/MS) for Higher Education Institutes 199\u003c\/p\u003e \u003cp\u003e11.3 Fuzzy TOPSIS 203\u003c\/p\u003e \u003cp\u003e11.3.1 Extension of TOPSIS Algorithm: Fuzzy TOPSIS 203\u003c\/p\u003e \u003cp\u003e11.4 Sample Numerical Illustration 207\u003c\/p\u003e \u003cp\u003e11.5 Conclusions 213\u003c\/p\u003e \u003cp\u003eReferences 213\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming 215\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKandarp Vidyasagar and Rajiv Kr. Dwivedi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 216\u003c\/p\u003e \u003cp\u003e12.1.1 Basic Concepts of Fuzzy AHP and Goal Programming 217\u003c\/p\u003e \u003cp\u003e12.2 Research Model 221\u003c\/p\u003e \u003cp\u003e12.2.1 Average Growth Rate Calculation 227\u003c\/p\u003e \u003cp\u003e12.3 Result and Discussion 233\u003c\/p\u003e \u003cp\u003e12.4 Conclusion 234\u003c\/p\u003e \u003cp\u003eReferences 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Evaluation of Ergonomic Design for the Visual Display Terminal Operator at Static Work Under FMCDM Environment 237\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBipradas Bairagi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 238\u003c\/p\u003e \u003cp\u003e13.2 Proposed Algorithm 240\u003c\/p\u003e \u003cp\u003e13.3 An Illustrative Example on Ergonomic Design Evaluation 245\u003c\/p\u003e \u003cp\u003e13.4 Conclusions 249\u003c\/p\u003e \u003cp\u003eReferences 249\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Optimization of Energy Generated from Ocean Wave Energy Using Fuzzy Logic 253\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. B. Goyal, Pradeep Bedi, Jugnesh Kumar and Prasenjit Chatterjee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 254\u003c\/p\u003e \u003cp\u003e14.2 Control Approach in Wave Energy Systems 255\u003c\/p\u003e \u003cp\u003e14.3 Related Work 257\u003c\/p\u003e \u003cp\u003e14.4 Mathematical Modeling for Energy Conversion from Ocean Waves 259\u003c\/p\u003e \u003cp\u003e14.5 Proposed Methodology 260\u003c\/p\u003e \u003cp\u003e14.5.1 Wave Parameters 261\u003c\/p\u003e \u003cp\u003e14.5.2 Fuzzy-Optimizer 262\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 264\u003c\/p\u003e \u003cp\u003eReferences 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 The m-Polar Fuzzy TOPSIS Method for NTM Selection 267\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMadan Jagtap and Prasad Karande\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 268\u003c\/p\u003e \u003cp\u003e15.2 Literature Review 268\u003c\/p\u003e \u003cp\u003e15.3 Methodology 270\u003c\/p\u003e \u003cp\u003e15.3.1 Steps of the mFS TOPSIS 270\u003c\/p\u003e \u003cp\u003e15.4 Case Study 272\u003c\/p\u003e \u003cp\u003e15.4.1 Effect of Analytical Hierarchy Process (AHP) Weight Calculation on the mFS TOPSIS Method 273\u003c\/p\u003e \u003cp\u003e15.4.2 Effect of Shannon’s Entropy Weight Calculation on the m-Polar Fuzzy Set TOPSIS Method 277\u003c\/p\u003e \u003cp\u003e15.5 Results and Discussions 281\u003c\/p\u003e \u003cp\u003e15.5.1 Result Validation 281\u003c\/p\u003e \u003cp\u003e15.6 Conclusions and Future Scope 283\u003c\/p\u003e \u003cp\u003eReferences 284\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology 287\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBipradas Bairagi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 288\u003c\/p\u003e \u003cp\u003e16.2 MCDM Techniques 289\u003c\/p\u003e \u003cp\u003e16.2.1 Fahp 289\u003c\/p\u003e \u003cp\u003e16.2.2 Entropy Method as Weights (Influence) Evaluation Technique 290\u003c\/p\u003e \u003cp\u003e16.3 The Proposed Hybrid and Super Hybrid FMCDM Approaches 291\u003c\/p\u003e \u003cp\u003e16.3.1 Topsis 291\u003c\/p\u003e \u003cp\u003e16.3.2 FMOORA Method 292\u003c\/p\u003e \u003cp\u003e16.3.3 FVIKOR 292\u003c\/p\u003e \u003cp\u003e16.3.4 Fuzzy Grey Theory (FGT) 293\u003c\/p\u003e \u003cp\u003e16.3.5 COPRAS –G 293\u003c\/p\u003e \u003cp\u003e16.3.6 Super Hybrid Algorithm 294\u003c\/p\u003e \u003cp\u003e16.4 Illustrative Example 295\u003c\/p\u003e \u003cp\u003e16.5 Results and Discussions 298\u003c\/p\u003e \u003cp\u003e16.5.1 FTOPSIS 298\u003c\/p\u003e \u003cp\u003e16.5.2 FMOORA 298\u003c\/p\u003e \u003cp\u003e16.5.3 FVIKRA 298\u003c\/p\u003e \u003cp\u003e16.5.4 Fuzzy Grey Theory (FGT) 299\u003c\/p\u003e \u003cp\u003e16.5.5 COPRAS-G 299\u003c\/p\u003e \u003cp\u003e16.5.6 Super Hybrid Approach (SHA) 299\u003c\/p\u003e \u003cp\u003e16.6 Conclusions 302\u003c\/p\u003e \u003cp\u003eReferences 302\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Fuzzy MCDM on CCPM for Decision Making: A Case Study 305\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBimal K. Jena, Biswajit Das, Amarendra Baral and Sushanta Tripathy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 306\u003c\/p\u003e \u003cp\u003e17.2 Literature Review 307\u003c\/p\u003e \u003cp\u003e17.3 Objective of Research 308\u003c\/p\u003e \u003cp\u003e17.4 Cluster Analysis 308\u003c\/p\u003e \u003cp\u003e17.4.1 Hierarchical Clustering 309\u003c\/p\u003e \u003cp\u003e17.4.2 Partitional Clustering 309\u003c\/p\u003e \u003cp\u003e17.5 Clustering 310\u003c\/p\u003e \u003cp\u003e17.6 Methodology 314\u003c\/p\u003e \u003cp\u003e17.7 TOPSIS Method 316\u003c\/p\u003e \u003cp\u003e17.8 Fuzzy TOPSIS Method 318\u003c\/p\u003e \u003cp\u003e17.9 Conclusion 325\u003c\/p\u003e \u003cp\u003e17.10 Scope of Future Study 326\u003c\/p\u003e \u003cp\u003eReferences 326\u003c\/p\u003e \u003cp\u003eIndex 329\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49989870944599,"sku":"9781119864929","price":133.2,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119864929.jpg?v=1739542052"},{"product_id":"neural-information-processing-30th-international-conference-iconip-2023-changsha-china-november-20-23-2023-proceedings-part-iii-9789819980666","title":"Neural Information Processing: 30th International","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe six-volume set LNCS 14447 until 14452 constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023. \u003cbr\u003eThe 652 papers presented in the proceedings set were carefully reviewed and selected from 1274 submissions. They focus on theory and algorithms, cognitive neurosciences; human centred computing; applications in neuroscience, neural networks, deep learning, and related fields. \u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eTheory and Algorithms.- Efficient Lightweight Network with Transformer-based Distillation for Micro-crack Detection of Solar Cells.- {MTLAN: Multi-Task Learning and Auxiliary Network for Enhanced Sentence Embedding.- Correlated Online k-Nearest Neighbors Regressor Chain for Online Multi-Output Regression.- Evolutionary Computation for Berth Allocation Problems: A Survey.- Cognitive Neurosciences.- Privacy-Preserving Travel Time Prediction for Internet of Vehicles: A Crowdsensing and Federated Learning Approach.- A Fine-Grained Domain Adaptation Method for Cross-Session Vigilance Estimation in SSVEP-Based BCI.- RMPE:Reducing Residual Membrane Potential Error for Enabling High-accuracy and Ultra-low-latency Spiking Neural Networks.- An improved target searching and imaging method for CSAR.- Block-Matching Multi-Pedestrian Tracking.- RPF3D: Range-Pillar Feature Deep Fusion 3D Detector for Autonomous Driving.- Traffic Signal Control Optimization Based on Deep Reinforcement Learning With Attention Mechanisms.- CMCI: A Robust Multimodal Fusion Method For Spiking Neural Networks.- A Weakly Supervised Deep Learning Model for Alzheimer's Disease Prognosis Using MRI and Incomplete Labels.- Two-Stream Spectral-Temporal Denoising Network for End-to-end Robust EEG-based Emotion Recognition.- Brain-inspired Binaural Sound Source Localization Method Based On Liquid State Machine.- A Causality-Based Interpretable Cognitive Diagnosis Model.- RoBrain: Towards Robust Brain-to-Image Reconstruction via Cross-Domain Contrastive Learning.- High-dimensional multi-objective PSO based on radial projection.- Link Prediction Based on the Sub-graphs Learning with Fused Features.- Naturalistic Emotion Recognition Using EEG and Eye Movements.- Task Scheduling With Improved Particle Swarm Optimization In Cloud Data Center.- Traffic Signal Optimization at T-shaped intersections Based on Deep Q Networks.- A Multi-task Framework for Solving Multimodal Multiobjective Optimization Problems.- Domain Generalized Object Detection with Triple Graph Reasoning Network.- RPUC: Semi-supervised 3D Biomedical Image Segmentation through Rectified Pyramid Unsupervised Consistency.- Cancellable iris recognition scheme based on inversion fusion and local ranking.- EWMIGCN: Emotional Weighting based Multimodal Interaction Graph Convolutional Networks for Personalized Prediction.- Neighborhood Learning for Artificial Bee Colony Algorithm: A Mini-survey.- Human Centred Computing.- Channel Attention Separable Convolution Network for Skin Lesion Segmentation.- A DNN-based Learning Framework for Continuous Movements Segmentation.- Neural-Symbolic Recommendation with Graph-Enhanced Information.- Contrastive Hierarchical Gating Networks for Rating Prediction.- Interactive Selection Recommendation Based on the Multi-Head Attention Graph Neural Network.- CM-TCN: Channel-aware Multi-scale Temporal Convolutional Networks For Speech Emotion Recognition.- FLDNet: A Foreground-Aware Network for Polyp Segmentation Leveraging Long-Distance Dependencies.- Domain-Invariant Task Optimization for Cross-domain Recommendation.- Ensemble of randomized neural network and boosted trees for eye tracking-based driver situation awareness recognition and interpretation.- Temporal Modeling Approach for Video Action Recognition Based on Vision-Language Models.- A Deep Learning Framework with Pruning RoI Proposal for Dental Caries Detection in Panoramic X-ray Images.- User stance aware network for rumor detection using semantic relation inference and temporal graph convolution.- IEEG-CT: A CNN and Transformer Based Method for Intracranial EEG Signal Classification.- Multi-Task Learning Network for Automatic Pancreatic Tumor Segmentation and Classification with Inter-Network Channel Feature Fusion.- Fast and Efficient Brain Extraction with Recursive MLP based 3D UNet.- A Hip-Knee Joint Coordination Evaluation System in Hemiplegic Individuals Based on Cyclogram Analysis.- Evaluation of football players' performance based on Multi-Criteria Decision Analysis approach and sensitivity analysis.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer Verlag, Singapore","offers":[{"title":"Default Title","offer_id":50473276670295,"sku":"9789819980666","price":75.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789819980666.jpg?v=1744905946"},{"product_id":"a-primer-on-machine-learning-applications-in-civil-engineering-9781138323391","title":"A Primer on Machine Learning Applications in","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eMachine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine-learning techniques that can be utilized for solving civil engineering problems. The fundamentals of both theoretical and practical aspects are discussed in the domains of water resources\/hydrological modeling, geotechnical engineering, construction engineering and management, and coastal\/marine engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, modeling evaporation, estimation of dew point temperature, modeling compressive strength of concrete, ground water level forecasting, and significant wave height forecasting are also included.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eFeatures\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003cli\u003eExclusive information on machine learning and data analytics applications with respect to civil engineering \u003c\/li\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003cli\u003eIncludes many machi\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e1. Introduction 2. Artificial Neural Networks 3. Fuzzy Logic 4. Support Vector Machine 5. Genetic Algorithm (GA) 6. Hybrid Systems 7. Data Statistics and Analytics 8. Applications in the Civil Engineering Domain 9. Conclusion and Future Scope of Work\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":50577820877143,"sku":"9781138323391","price":87.39,"currency_code":"GBP","in_stock":true}]},{"product_id":"a-first-course-in-fuzzy-logic-9781138585089","title":"A First Course in Fuzzy Logic","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003eA First Course in Fuzzy Logic, Fourth Edition\u003c\/em\u003e\u003c\/strong\u003e is an expanded version of the successful third edition. It provides a comprehensive introduction to the theory and applications of fuzzy logic.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis popular text offers a firm mathematical basis for the calculus of fuzzy concepts necessary for designing intelligent systems and a solid background for readers to pursue further studies and real-world applications.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cb\u003e\u003c\/b\u003e\u003cp\u003eNew in the Fourth Edition:\u003c\/p\u003e\u003cul\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003cli\u003eFeatures new results on fuzzy sets of type-2\u003c\/li\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003cli\u003eProvides more information on copulas for modeling dependence structures\u003c\/li\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003cli\u003eIncludes quantum probability for uncertainty modeling in social sciences, especially in economics\u003c\/li\u003e\n\u003cp\u003e\u003c\/p\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eWith its comprehensive updates, this new edition presents all the background necessary for students, instructors and professionals to begin using fuzzy logic in its manyapplications in computer science, mathema\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eThe Concept of Fuzziness\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eExamples. Mathematical modeling. Some operations on fuzzy sets. Fuzziness as uncertainty.\u003c\/p\u003e\u003cp\u003eSome Algebra of Fuzzy Sets\u003c\/p\u003e\u003cp\u003eBoolean algebras and lattices. Equivalence relations and partitions. Composing mappings. Isomorphisms and homomorphisms. Alpha-cuts. Images of alpha-level sets.\u003c\/p\u003e\u003cp\u003eFuzzy Quantities\u003c\/p\u003e\u003cp\u003eFuzzy quantities. Fuzzy numbers. Fuzzy intervals. \u003c\/p\u003e\u003cp\u003eLogical Aspects of Fuzzy Sets\u003c\/p\u003e\u003cp\u003eClassical two-valued logic. A three-valued logic. Fuzzy logic. Fuzzy and Lukasiewicz logics. Interval-valued fuzzy logic.\u003c\/p\u003e\u003cp\u003eBasic Connectives\u003c\/p\u003e\u003cp\u003et-norms. Generators of t-norms. Isomorphisms of t-norms. Negations. Nilpotent t-norms and negations. T-conforms. De Morgan systems. Groups and t-norms. Interval-valued fuzzy sets. Type-2 fuzzy sets.\u003c\/p\u003e\u003cp\u003eAdditional Topics on Connectives\u003c\/p\u003e\u003cp\u003eFuzzy implications. Averaging operators. Powers of t-norms. Sensitivity of connectives. Copulas and t-norms.\u003c\/p\u003e\u003cp\u003eFuzzy Relations\u003c\/p\u003e\u003cp\u003eDefinitions and examples. Binary fuzzy relations. Operations on fuzzy relations. Fuzzy partitions. Fuzzy relations as Chu spaces. Approximate reasoning. Approximate reasoning in expert systems. A simple form of generalized modus ponens. The compositional rule of inference.\u003c\/p\u003e\u003cp\u003eUniversal Approximation \u003c\/p\u003e\u003cp\u003eFuzzy rule bases. Design methodologies. Some mathematical background. Approximation capability. \u003c\/p\u003e\u003cp\u003ePossibility Theory\u003c\/p\u003e\u003cp\u003eProbability and uncertainty. Random sets. Possibility measures. \u003c\/p\u003e\u003cp\u003ePartial Knowledge\u003c\/p\u003e\u003cp\u003eMotivations. Belief functions and incidence algebras. Monotonicity. Beliefs, densities, and allocations. Belief functions on infinite sets. Mobius transforms of set-functions. Reasoning with belief functions. Decision making using belief functions. Rough sets. Conditional events.\u003c\/p\u003e\u003cp\u003eFuzzy Measures\u003c\/p\u003e\u003cp\u003eMotivation and definitions. Fuzzy measures and lower probabilities. Fuzzy measures in other areas. Conditional fuzzy measures.\u003c\/p\u003e\u003cp\u003eThe Choquet Integral\u003c\/p\u003e\u003cp\u003eThe Lebesgue integral. The Sugeno integral. The Choquet integral. \u003c\/p\u003e\u003cp\u003eFuzzy Modeling and Control\u003c\/p\u003e\u003cp\u003eMotivation for fuzzy control. The methodology of fuzzy control. Optimal fuzzy control. An analysis of fuzzy control techniques.\u003c\/p\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":50577833001303,"sku":"9781138585089","price":114.0,"currency_code":"GBP","in_stock":true}]},{"product_id":"artificial-neural-networks-for-engineering-applications-9780128182475","title":"Artificial Neural Networks for Engineering","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Hierarchical Dynamic Neural Networks for Cascade System Modeling with Application to Wastewater Treatment 2. Hyperellipsoidal Neural Network trained with Extended Kalman Filter for forecasting of time series 3. Neural networks: a methodology for modeling and control design of dynamical systems 4. Continuous–Time Decentralized Neural Control of a Quadrotor UAV 5. Support Vector Regression for digital video processing 6. Artificial Neural Networks Based on Nonlinear Bioprocess Models for Predicting Wastewater Organic Compounds and Biofuels Production 7. Neural Identification for Within-Host Infectious Disease Progression 8. Attack Detection and Estimation for Cyber-physical Systems by using Learning Methodology 9. Adaptive PID Controller using a Multilayer Perceptron Trained with the Extended Kalman Filter for an Unmanned Aerial Vehicle 10. Sensitivity Analysis with Artificial Neural Networks for Operation of Photovoltaic Systems 11. Pattern Classification and its Applications to Control of Biomechatronic Systems","brand":"Elsevier Science","offers":[{"title":"Default Title","offer_id":51017548759383,"sku":"9780128182475","price":94.95,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780128182475.jpg?v=1750773909"},{"product_id":"ai-and-deep-learning-in-biometric-security-trends-potential-and-challenges-artificial-intelligence-ai-elementary-to-advanced-practices-9780367422448","title":"AI and Deep Learning in Biometric Security Trends","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book provides an in-depth overview of artificial intelligence and deep learning approaches with case studies to solve problems associated with biometric security such as authentication, indexing, template protection, spoofing attack detection, ROI detection, gender classification etc. \u003c\/p\u003e\u003cp\u003eThis text highlights a showcase of cutting-edge research on the use of convolution neural networks, autoencoders, recurrent convolutional neural networks in face, hand, iris, gait, fingerprint, vein, and medical biometric traits. It also provides a step-by-step guide to understanding deep learning concepts for biometrics authentication approaches and presents an analysis of biometric images under various environmental conditions. \u003c\/p\u003e\u003cp\u003eThis book is sure to catch the attention of scholars, researchers, practitioners, and technology aspirants who are willing to research in the field of AI and biometric security.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e1. Deep Learning-Based Hyperspectral Multimodal Biometric Authentication System Using Palmprint and Dorsal Hand Vein. 2. Cancelable Biometrics for Template Protection: Future Directives with Deep Learning. 3. On Training Generative Adversarial Network for Enhancement of Latent Fingerprints. 4. DeepFake Face Video Detection Using Hybrid Deep Residual Networks nad LSTM Architecture. 5. Multi-spectral Short-Wave Infrared Sensors and Convolutional Neural Networks for Biometric Presentation Attack Detection. 6. AI-Based Approach for Person Identification Using ECG Biometric. 7. Cancelable Biometric Systems from Research to Reality: The Road Less Travelled. 8. Gender Classification under Eyeglass Occluded Ocular Region: An Extensive Study Using Multi-spectral Imaging. 9. Investigation of the Fingernail Plate for Biometric Authentication using Deep Neural Networks. 10. Fraud Attack Detection in Remote Verification systems for Non-enrolled Users. 11. Indexing on Biometric Databases. 12. Iris Segmentation in the Wild Using Encoder-Decoder-Based Deep Learning Techniques. 13. PPG-Based Biometric Recognition: Opportunities with Machine and Deep Learning. 14. Current Trends of Machine Learning Techniques in Biometrics and its Applications.\u003c\/p\u003e","brand":"Taylor \u0026 Francis Ltd (Sales)","offers":[{"title":"Default Title","offer_id":51017896690007,"sku":"9780367422448","price":150.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780367422448.jpg?v=1750775013"},{"product_id":"stochastic-optimization-for-largescale-machine-learning-9781032131757","title":"Stochastic Optimization for Largescale Machine","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAdvancements in the technology and availability of data sources have led to the `Big Data'' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems.\u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eStochastic Optimization for Large-scale Machine Learning \u003c\/i\u003e\u003c\/b\u003eidentifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods.\u003c\/p\u003e\u003cp\u003eKey Features:\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eBridges machine learning and Optimisation.\u003c\/li\u003e\n\u003cli\u003eBridges theory and practice in machine learning.\u003c\/li\u003e\n\u003cli\u003eIdentifies key re\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eList of Figures\u003cbr\u003eList of Tables\u003cbr\u003ePreface \u003c\/p\u003e\n\u003cp\u003eSection I BACKGROUND\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eIntroduction\u003c\/b\u003e\u003cbr\u003e1.1 LARGE-SCALE MACHINE LEARNING \u003cbr\u003e1.2 OPTIMIZATION PROBLEMS \u003cbr\u003e1.3 LINEAR CLASSIFICATION\u003cbr\u003e1.3.1 Support Vector Machine (SVM) \u003cbr\u003e1.3.2 Logistic Regression \u003cbr\u003e1.3.3 First and Second Order Methods\u003cbr\u003e1.3.3.1 First Order Methods \u003cbr\u003e1.3.3.2 Second Order Methods \u003cbr\u003e1.4 STOCHASTIC APPROXIMATION APPROACH \u003cbr\u003e1.5 COORDINATE DESCENT APPROACH \u003cbr\u003e1.6 DATASETS \u003cbr\u003e1.7 ORGANIZATION OF BOOK \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eOptimisation Problem, Solvers, Challenges and Research Directions\u003c\/b\u003e\u003cbr\u003e2.1 INTRODUCTION \u003cbr\u003e2.1.1 Contributions \u003cbr\u003e2.2 LITERATURE \u003cbr\u003e2.3 PROBLEM FORMULATIONS \u003cbr\u003e2.3.1 Hard Margin SVM (1992) \u003cbr\u003e2.3.2 Soft Margin SVM (1995) \u003cbr\u003e2.3.3 One-versus-Rest (1998) \u003cbr\u003e2.3.4 One-versus-One (1999) \u003cbr\u003e2.3.5 Least Squares SVM (1999) \u003cbr\u003e2.3.6 v-SVM (2000) \u003cbr\u003e2.3.7 Smooth SVM (2001) \u003cbr\u003e2.3.8 Proximal SVM (2001) \u003cbr\u003e2.3.9 Crammer Singer SVM (2002) \u003cbr\u003e2.3.10 Ev-SVM (2003) \u003cbr\u003e2.3.11 Twin SVM (2007) \u003cbr\u003e2.3.12 Capped lp-norm SVM (2017) \u003cbr\u003e2.4 PROBLEM SOLVERS \u003cbr\u003e2.4.1 Exact Line Search Method \u003cbr\u003e2.4.2 Backtracking Line Search \u003cbr\u003e2.4.3 Constant Step Size \u003cbr\u003e2.4.4 Lipschitz \u0026amp; Strong Convexity Constants \u003cbr\u003e2.4.5 Trust Region Method \u003cbr\u003e2.4.6 Gradient Descent Method \u003cbr\u003e2.4.7 Newton Method \u003cbr\u003e2.4.8 Gauss-Newton Method \u003cbr\u003e2.4.9 Levenberg-Marquardt Method \u003cbr\u003e2.4.10 Quasi-Newton Method \u003cbr\u003e2.4.11 Subgradient Method \u003cbr\u003e2.4.12 Conjugate Gradient Method \u003cbr\u003e2.4.13 Truncated Newton Method \u003cbr\u003e2.4.14 Proximal Gradient Method \u003cbr\u003e2.4.15 Recent Algorithms \u003cbr\u003e2.5 COMPARATIVE STUDY \u003cbr\u003e2.5.1 Results from Literature \u003cbr\u003e2.5.2 Results from Experimental Study \u003cbr\u003e2.5.2.1 Experimental Setup and Implementation Details \u003cbr\u003e2.5.2.2 Results and Discussions \u003cbr\u003e2.6 CURRENT CHALLENGES AND RESEARCH DIRECTIONS \u003cbr\u003e2.6.1 Big Data Challenge \u003cbr\u003e2.6.2 Areas of Improvement \u003cbr\u003e2.6.2.1 Problem Formulations \u003cbr\u003e2.6.2.2 Problem Solvers \u003cbr\u003e2.6.2.3 Problem Solving Strategies\/Approaches \u003cbr\u003e2.6.2.4 Platforms\/Frameworks \u003cbr\u003e2.6.3 Research Directions \u003cbr\u003e2.6.3.1 Stochastic Approximation Algorithms \u003cbr\u003e2.6.3.2 Coordinate Descent Algorithms \u003cbr\u003e2.6.3.3 Proximal Algorithms \u003cbr\u003e2.6.3.4 Parallel\/Distributed Algorithms \u003cbr\u003e2.6.3.5 Hybrid Algorithms \u003cbr\u003e2.7 CONCLUSION \u003c\/p\u003e\n\u003cp\u003eSection II FIRST ORDER METHODS\u003cbr\u003e\u003cb\u003eMini-batch and Block-coordinate Approach \u003c\/b\u003e\u003cbr\u003e3.1 INTRODUCTION \u003cbr\u003e3.1.1 Motivation \u003cbr\u003e3.1.2 Batch Block Optimization Framework (BBOF) \u003cbr\u003e3.1.3 Brief Literature Review \u003cbr\u003e3.1.4 Contributions \u003cbr\u003e3.2 STOCHASTIC AVERAGE ADJUSTED GRADIENT (SAAG) METHODS\u003cbr\u003e3.3 ANALYSIS \u003cbr\u003e3.4 NUMERICAL EXPERIMENTS \u003cbr\u003e3.4.1 Experimental setup \u003cbr\u003e3.4.2 Convergence against epochs \u003cbr\u003e3.4.3 Convergence against Time \u003cbr\u003e3.5 CONCLUSION AND FUTURE SCOPE \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eVariance Reduction Methods \u003c\/b\u003e\u003cbr\u003e4.1 INTRODUCTION \u003cbr\u003e4.1.1 Optimization Problem \u003cbr\u003e4.1.2 Solution Techniques for Optimization Problem \u003cbr\u003e4.1.3 Contributions \u003cbr\u003e4.2 NOTATIONS AND RELATED WORK \u003cbr\u003e4.2.1 Notations \u003cbr\u003e4.2.2 Related Work \u003cbr\u003e4.3 SAAG-I, II AND PROXIMAL EXTENSIONS \u003cbr\u003e4.4 SAAG-III AND IV ALGORITHMS \u003cbr\u003e4.5 ANALYSIS \u003cbr\u003e4.6 EXPERIMENTAL RESULTS \u003cbr\u003e4.6.1 Experimental Setup \u003cbr\u003e4.6.2 Results with Smooth Problem \u003cbr\u003e4.6.3 Results with non-smooth Problem \u003cbr\u003e4.6.4 Mini-batch Block-coordinate versus mini-batch setting \u003cbr\u003e4.6.5 Results with SVM \u003cbr\u003e4.7 CONCLUSION \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eLearning and Data Access \u003c\/b\u003e\u003cbr\u003e5.1 INTRODUCTION \u003cbr\u003e5.1.1 Optimization Problem \u003cbr\u003e5.1.2 Literature Review \u003cbr\u003e5.1.3 Contributions \u003cbr\u003e5.2 SYSTEMATIC SAMPLING \u003cbr\u003e5.2.1 Definitions \u003cbr\u003e5.2.2 Learning using Systematic Sampling \u003cbr\u003e5.3 ANALYSIS \u003cbr\u003e5.4 EXPERIMENTS \u003cbr\u003e5.4.1 Experimental Setup \u003cbr\u003e5.4.2 Implementation Details \u003cbr\u003e5.4.3 Results \u003cbr\u003e5.5 CONCLUSION \u003c\/p\u003e\n\u003cp\u003eSection III SECOND ORDER METHODS\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eMini-batch Block-coordinate Newton Method \u003c\/b\u003e\u003cbr\u003e6.1 INTRODUCTION \u003cbr\u003e6.1.1 Contributions \u003cbr\u003e6.2 MBN \u003cbr\u003e6.3 EXPERIMENTS \u003cbr\u003e6.3.1 Experimental Setup \u003cbr\u003e6.3.2 Comparative Study \u003cbr\u003e6.4 CONCLUSION \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eStochastic Trust Region Inexact Newton Method \u003c\/b\u003e\u003cbr\u003e7.1 INTRODUCTION \u003cbr\u003e7.1.1 Optimization Problem \u003cbr\u003e7.1.2 Solution Techniques \u003cbr\u003e7.1.3 Contributions \u003cbr\u003e7.2 LITERATURE REVIEW \u003cbr\u003e7.3 TRUST REGION INEXACT NEWTON METHOD \u003cbr\u003e7.3.1 Inexact Newton Method \u003cbr\u003e7.3.2 Trust Region Inexact Newton Method \u003cbr\u003e7.4 STRON \u003cbr\u003e7.4.1 Complexity \u003cbr\u003e7.4.2 Analysis \u003cbr\u003e7.5 EXPERIMENTAL RESULTS \u003cbr\u003e7.5.1 Experimental Setup \u003cbr\u003e7.5.2 Comparative Study \u003cbr\u003e7.5.3 Results with SVM \u003cbr\u003e7.6 EXTENSIONS \u003cbr\u003e7.6.1 PCG Subproblem Solver 1\u003cbr\u003e7.6.2 Stochastic Variance Reduced Trust Region Inexact Newton Method \u003cbr\u003e7.7 CONCLUSION \u003c\/p\u003e\n\u003cp\u003eSection IV CONCLUSION\u003cbr\u003e\u003cb\u003eConclusion and Future Scope \u003c\/b\u003e\u003cbr\u003e8.1 FUTURE SCOPE 142\u003c\/p\u003e\n\u003cp\u003eBibliography\u003c\/p\u003e\n\u003cp\u003eIndex\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":51018857185623,"sku":"9781032131757","price":135.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781032131757.jpg?v=1750778412"},{"product_id":"cognitive-and-neural-modelling-for-visual-information-representation-and-memorization-9781032249117","title":"Cognitive and Neural Modelling for Visual","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eFocusing on how visual information is represented, stored and extracted in the human brain, this book uses cognitive neural modeling in order to show how visual information is represented and memorized in the brain. \u003c\/p\u003e\u003cp\u003eBreaking through traditional visual information processing methods, the author combines our understanding of perception and memory from the human brain with computer vision technology, and provides a new approach for image recognition and classification. While biological visual cognition models and human brain memory models are established, applications such as pest recognition and carrot detection are also involved in this book.\u003c\/p\u003e\u003cp\u003eGiven the range of topics covered, this book is a valuable resource for students, researchers and practitioners interested in the rapidly evolving field of neurocomputing, computer vision and machine learning.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction  2. Methods of visual perception and memory modeling  3. Bio-inspired model for object recognition based on histogram of oriented gradients  4. Modeling object recognition in visual cortex using multiple firing K-means and non-negative sparse coding  5. Biological modeling of human visual system using GLoP filters and sparse coding on multi-manifolds  6. Increment learning and rapid retrieval of visual information based on pattern association memory  7. Memory modeling based on free energy theory and restricted Boltzmann machine  8. Research on insect pest image detection and recognition based on bio-inspired methods  9. Carrot defect detection and grading based on computer vision and deep learning","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":51018923639127,"sku":"9781032249117","price":74.09,"currency_code":"GBP","in_stock":true}]},{"product_id":"artificial-intelligence-9781408225745","title":"Artificial Intelligence","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eDr Michael Negnevitsky \u003c\/b\u003eis a Professor in Electrical Engineering and Computer Science at the University of Tasmania, Australia. The book has developed from his lectures to undergraduates. Educated as an electrical engineer, Dr Negnevitsky's many interests include artificial intelligence and soft computing. His research involves the development and application of intelligent systems in electrical engineering, process control and environmental engineering. He has authored and co-authored over 300 research publications including numerous journal articles, four patents for inventions and two books.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003e\u003cem\u003e“This book covers many areas related to my module. I would be happy to recommend this book to my students. I believe my students would be able to follow this book without any difficulty. Book chapters are very well organised and this will help me to pick and choose the subjects related to this module.”\u003c\/em\u003e\u003c\/strong\u003e Dr Ahmad Lotfi, Nottingham Trent University, UK\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eContents\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePreface                                                                                    xii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eNew to this edition                                                                            xiii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eOverview of the book                                                           xiv\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAcknowledgements                                                                          xvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1        \u003c\/b\u003e\u003cb\u003eIntroduction to knowledge-based intelligent systems                      \u003c\/b\u003e          \u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1.1\u003c\/b\u003e     Intelligent machines, or what machines can do                            1\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1.2\u003c\/b\u003e     The history of artificial intelligence, or from the ‘Dark Ages’\u003c\/p\u003e \u003cp\u003e          to knowledge-based systems                                                       4\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1.3\u003c\/b\u003e     Summary                                                                         17\u003c\/p\u003e \u003cp\u003e          Questions for review                                                                   21\u0026lt;\u003c\/p\u003e","brand":"Pearson Education","offers":[{"title":"Default Title","offer_id":51019800510807,"sku":"9781408225745","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"machine-learning-an-essential-guide-to-machine-learning-for-beginners-who-want-to-understand-applications-artificial-intelligence-data-mining-big-data-and-more-9781647484385","title":"Machine Learning: An Essential Guide to Machine","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Bravex Publications","offers":[{"title":"Default Title","offer_id":51020229083479,"sku":"9781647484385","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"advanced-deep-learning-with-r-become-an-expert-at-designing-building-and-improving-advanced-neural-network-models-using-r-9781789538779","title":"Advanced Deep Learning with R: Become an expert","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eDiscover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries\u003c\/b\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eImplement deep learning algorithms to build AI models with the help of tips and tricks\u003c\/li\u003e\n\u003cli\u003eUnderstand how deep learning models operate using expert techniques\u003c\/li\u003e\n\u003cli\u003eApply reinforcement learning, computer vision, GANs, and NLP using a range of datasets\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eDeep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them.\u003c\/p\u003e\u003cp\u003eThis deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. \u003c\/p\u003e\u003cp\u003eBy the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eLearn how to create binary and multi-class deep neural network models\u003c\/li\u003e\n\u003cli\u003eImplement GANs for generating new images\u003c\/li\u003e\n\u003cli\u003eCreate autoencoder neural networks for image dimension reduction, image de-noising and image correction\u003c\/li\u003e\n\u003cli\u003eImplement deep neural networks for performing efficient text classification\u003c\/li\u003e\n\u003cli\u003eLearn to define a recurrent convolutional network model for classification in Keras\u003c\/li\u003e\n\u003cli\u003eExplore best practices and tips for performance optimization of various deep learning models\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eThis book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. 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This book will take you on a step-by-step practical journey, covering everything from the mathematical and theoretical aspects of neural networks, to building your own deep neural networks into your applications with the C# and .NET frameworks.\u003c\/p\u003e\u003cp\u003eThis book begins by giving you a quick refresher of neural networks. You will learn how to build a neural network from scratch using packages such as Encog, Aforge, and Accord. You will learn about various concepts and techniques, such as deep networks, perceptrons, optimization algorithms, convolutional networks, and autoencoders. You will learn ways to add intelligent features to your .NET apps, such as facial and motion detection, object detection and labeling, language understanding, knowledge, and intelligent search.\u003c\/p\u003e\u003cp\u003eThroughout this book, you will be working on interesting demonstrations that will make it easier to implement complex neural networks in your enterprise applications.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eUnderstand perceptrons and how to implement them in C#\u003c\/li\u003e\n\u003cli\u003eLearn how to train and visualize a neural network using cognitive services\u003c\/li\u003e\n\u003cli\u003ePerform image recognition for detecting and labeling objects using C# and TensorFlowSharp\u003c\/li\u003e\n\u003cli\u003eDetect specific image characteristics such as a face using Accord.Net\u003c\/li\u003e\n\u003cli\u003eDemonstrate particle swarm optimization using a simple XOR problem and Encog\u003c\/li\u003e\n\u003cli\u003eTrain convolutional neural networks using ConvNetSharp\u003c\/li\u003e\n\u003cli\u003eFind optimal parameters for your neural network functions using numeric and heuristic optimization techniques.\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eThis book is for Machine Learning Engineers, Data Scientists, Deep Learning Aspirants and Data Analysts who are now looking to move into advanced machine learning and deep learning with C#. Prior knowledge of machine learning and working experience with C# programming is required to take most out of this book\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eTable of Contents\u003col\u003e\n\u003cli\u003eA Quick Refresher\u003c\/li\u003e\n\u003cli\u003eBuilding our first Neural Network Together\u003c\/li\u003e\n\u003cli\u003eDecision Tress and Random Forests\u003c\/li\u003e\n\u003cli\u003eFace and Motion Detection\u003c\/li\u003e\n\u003cli\u003eTraining CNNs using ConvNetSharp\u003c\/li\u003e\n\u003cli\u003eTraining Autoencoders Using RNNSharp\u003c\/li\u003e\n\u003cli\u003eReplacing Back Propagation with PSO\u003c\/li\u003e\n\u003cli\u003eFunction Optimizations; How and Why\u003c\/li\u003e\n\u003cli\u003eFinding Optimal Parameters\u003c\/li\u003e\n\u003cli\u003eObject Detection with TensorFlowSharp\u003c\/li\u003e\n\u003cli\u003eTime Series Prediction and LSTM Using CNTK\u003c\/li\u003e\n\u003cli\u003eGRUs Compared to LSTMs, RNNs, and Feedforward Networks\u003c\/li\u003e\n\u003cli\u003eAppendix A- Activation Function Timings\u003c\/li\u003e\n\u003cli\u003eAppendix B-  Function Optimization Reference\u003c\/li\u003e\n\u003c\/ol\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":51020502171991,"sku":"9781789612011","price":29.44,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781789612011.jpg?v=1750783574"},{"product_id":"the-the-reinforcement-learning-workshop-learn-how-to-apply-cutting-edge-reinforcement-learning-algorithms-to-a-wide-range-of-control-problems-9781800200456","title":"The The Reinforcement Learning Workshop: Learn","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eStart with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide\u003c\/b\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eUse TensorFlow to write reinforcement learning agents for performing challenging tasks\u003c\/li\u003e\n\u003cli\u003eLearn how to solve finite Markov decision problems\u003c\/li\u003e\n\u003cli\u003eTrain models to understand popular video games like Breakout\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eVarious intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models.\u003c\/p\u003e\u003cp\u003eStarting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you’ll find out when to use a policy-based method to tackle an RL problem.\u003c\/p\u003e\u003cp\u003eBy the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eUse OpenAI Gym as a framework to implement RL environments\u003c\/li\u003e\n\u003cli\u003eFind out how to define and implement reward function\u003c\/li\u003e\n\u003cli\u003eExplore Markov chain, Markov decision process, and the Bellman equation\u003c\/li\u003e\n\u003cli\u003eDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning\u003c\/li\u003e\n\u003cli\u003eUnderstand the multi-armed bandit problem and explore various strategies to solve it\u003c\/li\u003e\n\u003cli\u003eBuild a deep Q model network for playing the video game Breakout\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eIf you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eTable of Contents\u003col\u003e\n\u003cli\u003eIntroduction to Reinforcement Learning\u003c\/li\u003e\n\u003cli\u003eMarkov Decision Processes and Bellman Equations\u003c\/li\u003e\n\u003cli\u003eDeep Learning in Practice with TensorFlow 2\u003c\/li\u003e\n\u003cli\u003eGetting Started with OpenAI and TensorFlow for Reinforcement Learning\u003c\/li\u003e\n\u003cli\u003eDynamic Programming\u003c\/li\u003e\n\u003cli\u003eMonte Carlo Methods\u003c\/li\u003e\n\u003cli\u003eTemporal Difference Learning\u003c\/li\u003e\n\u003cli\u003eThe Multi-Armed Bandit Problem\u003c\/li\u003e\n\u003cli\u003eWhat Is Deep Q Learning?\u003c\/li\u003e\n\u003cli\u003ePlaying an Atari Game with Deep Recurrent Q Networks\u003c\/li\u003e\n\u003cli\u003ePolicy-Based Methods for Reinforcement Learning\u003c\/li\u003e\n\u003cli\u003eEvolutionary Strategies for RL\u003c\/li\u003e\n\u003c\/ol\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":51020517343575,"sku":"9781800200456","price":34.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781800200456.jpg?v=1750783616"},{"product_id":"ai-for-finance-9781032391205","title":"AI for Finance","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eFinance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance.\u003c\/p\u003e\u003cp\u003eIncluding original research, the book explains the impact of ignoring computation in classical economics; examines the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists; and introduces Directional Change and explains how this can be used.\u003c\/p\u003e\u003cp\u003eTo finance students and practitioners, this book will explain the promise of AI, as well as its limitations. It will cover knowledge representation, modelling, simulation and machine learning, explaining the principles of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has mad\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e“This important book is an unusually topical attempt to introduce readers to the relationship between the technical analysis of financial market prices and the automated implementation of its findings. The book will be of considerable interest to those who wish to know about this relationship in an eminently readable form: both professional financial market analysts and those considering future employment in the field.” \u003ci\u003e-\u003c\/i\u003e\u003ci\u003e-\u003c\/i\u003e\u003cb\u003e\u003ci\u003eMichael Dempster\u003c\/i\u003e\u003c\/b\u003e\u003ci\u003e, ‎Professor Emeritus in the Statistical Laboratory at the University of Cambridge\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e“AI is an important part of finance today. Students who want to join the finance industry should read this book. The trained eyes will also find a lot of insights in the book. I cannot think of any other book that teaches computational finance at a beginner's level but at the same time is useful to practitioners.” --\u003ci\u003e\u003cb\u003eAmadeo Alentorn\u003c\/b\u003e, PhD, Head of Systematic Equities at Jupiter Asset Management\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e\"\u003cb\u003e\u003ci\u003eAI for Finance\u003c\/i\u003e\u003c\/b\u003e is an excellent primer for experts and newcomers seeking to unlock the potential of AI. The book combines deep thinking with a bird’s eye view of the whole field - the ideal text to get inspired and apply AI. A big thank you to Edward Tsang, a pioneer of AI and quantitative finance, for making the concepts and usage of AI easily accessible to academics and practitioners.\" --\u003cb\u003e\u003ci\u003eRichard Olsen\u003c\/i\u003e\u003c\/b\u003e\u003ci\u003e, Founder and CEO of Lykke, co-founder of OANDA, and pioneer in high frequency finance and fintech\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e“Without a doubt, AI symbolizes the future of finance and, in this important book, Professor Tsang provides an excellent account of its mechanics, concepts and strategies. Books featuring AI in finance are rare so practitioners and students would do well to read it to gain focus and valuable insights into this fast-evolving technology. Congratulations to Professor Tsang for providing a readable and engaging work in a complex technology that will appeal to all levels of readers!” \u003ci\u003e--\u003cb\u003eDr David Norman\u003c\/b\u003e, Founder of the TTC Institute\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e\"The use of AI\/ML in the financial industry is now more than a hype. In financial institutions there are numerous active transformation programs to introduce AI\/ML enabled products in areas such as risk, trading and advanced analytics. In this book, Edward, one of the early adopters of AI in finance, has provided an insightful guide for both finance practitioners and academics. I can see this book becoming a major reference in real-world applied AI in finance. Directional Change (Chapter 6) should be of particular interest to data scientists in finance, as how one collects data determines what one can reason about.\" -- \u003cstrong\u003e\u003cem\u003eDr Ali Rais Shaghaghi\u003c\/em\u003e\u003c\/strong\u003e, \u003cem\u003eLead Data Scientist at NatWest Group.\u003c\/em\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e1. AI-Finance Synergy, 2. Machine Learning Knows No Boundaries?, 3.Machine Learning in Finance, 4. Modelling, Simulation and Machine Learning, 5. Portfolio Optimization, 6. Financial Data: Beyond Time Series, 7. Over the Horizon \u003c\/p\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":51039188123991,"sku":"9781032391205","price":114.0,"currency_code":"GBP","in_stock":true}]},{"product_id":"principles-of-soft-computing-using-python-programming-9781394173136","title":"Principles of Soft Computing Using Python","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePrinciples of Soft Computing Using Python Programming\u003c\/b\u003e \u003cp\u003e\u003cb\u003eAn accessible guide to the revolutionary techniques of soft computing\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eSoft computing is a computing approach designed to replicate the human mind's unique capacity to integrate uncertainty and imprecision into its reasoning. It is uniquely suited to computing operations where rigid analytical models will fail to account for the variety and ambiguity of possible solutions. As machine learning and artificial intelligence become more and more prominent in the computing landscape, the potential for soft computing techniques to revolutionize computing has never been greater. \u003c\/p\u003e\u003cp\u003e\u003ci\u003ePrinciples of Soft Computing Using Python Programming\u003c\/i\u003e provides readers with the knowledge required to apply soft computing models and techniques to real computational problems. 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The 16 revised full papers presented were carefully reviewed and selected from 24 submissions. The conference presents papers on subject such as pattern recognition and machine learning based on artificial neural networks.\u003cp\u003e\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eTransformer-Encoder generated context-aware embeddings for spell correction.- Graph Augmentation for Neural Networks Using Matching-Graphs.- Wavelet Scattering Transform Depth Benefit, An Application for Speaker Identification.- Assessment of Pharmaceutical Patent Novelty using Siamese Neural Network.- A Review of Capsule Networks in Medical Image Analysis.- Multi-stage Bias Mitigation for Individual Fairness in Algorithmic Decisions.- Introducing an Atypical Loss: A Perceptual Metric Learning for Image Pairing.- A Study on the Autonomous Detection of Impact Craters.- Minimizing Cross Intersections in Graph Drawing via Linear Splines.- Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech.- Do Minimal Complexity Least Squares Support Vector Machines Work?.- A Novel Representation of Graphical Patterns for Graph Convolution Networks.- Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi Utilization of Vision Transformer for Classification and Ranking of Video Distortions.- White Blood Cell Classification of Porcine Blood Smear Images.- Medical Deepfake Detection using 3-Dimensional Neural Learning.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":51742897209687,"sku":"9783031206498","price":999.99,"currency_code":"GBP","in_stock":false}]},{"product_id":"statistical-physics-of-spin-glasses-and-information-processing-9780198509417","title":"Statistical Physics of Spin Glasses and Information Processing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eSpin glasses are magnetic materials. Statistical mechanics, a subfield of physics, has been a powerful tool to theoretically analyse various unique properties of spin glasses. A number of new analytical techniques have been developed to establish a theory of spin glasses. Surprisingly, these techniques have turned out to offer new tools and viewpoints for the understanding of information processing problems, including neural networks, error-correcting codes, image restoration, and optimization problems. This book is one of the first publications of the past ten years that provide a broad overview of this interdisciplinary field. Most of the book is written in a self-contained manner, assuming only a general knowledge of statistical mechanics and basic probability theory. It provides the reader with a sound introduction to the field and to the analytical techniques necessary to follow its most recent developments.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e... very enjoyable to read and often opening the reader's eye to new possibilities. This is a perfect introduction to the field for students and researchers who want to study problems in information science, including the use of physics in information processing * Butsuri *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Mean-field theory of phase transitions ; 2. Mean-field theory of spin glasses ; 3. Replica symmetry breaking ; 4. Gauge theory of spin glasses ; 5. Error-correcting codes ; 6. Image restoration ; 7. Associative memory ; 8. Learning in perceptron ; 9. Optimization problems ; A. Eigenvalues of the Hessian ; B. Parisi equation ; C. Channel coding theorem ; D. Distribution and free energy of K-Sat ; References ; Index","brand":"Clarendon Press","offers":[{"title":"Default Title","offer_id":51766948659543,"sku":"9780198509417","price":102.5,"currency_code":"GBP","in_stock":true}]},{"product_id":"artificial-neural-networks-in-food-processing-modeling-and-predictive-control-9783110645941","title":"Artificial Neural Networks in Food Processing: Modeling and Predictive Control","description":"","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":51864333353303,"sku":"9783110645941","price":71.1,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783110645941.jpg?v=1759921895"},{"product_id":"neural-networks-modeling-and-control-9780128170786","title":"Neural Networks Modeling and Control","description":"\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction2. Mathematical preliminaries3. Recurrent high order neural network identification of nonlinear discrete-time unknown system with time-delays4. Neural identifier-control scheme for nonlinear discrete-time unknown system with time-delays5. Recurrent high order neural network observer of nonlinear discrete-time unknown systems with time-delays6. Neural observer-control scheme for nonlinear discrete-time unknown system with time-delays7. Concluding remarks and future trends   AppendixA. Artificial neural networksB. Linear induction motor prototypeC. Differential robot prototype","brand":"Elsevier Science","offers":[{"title":"Default Title","offer_id":52083807912279,"sku":"9780128170786","price":103.5,"currency_code":"GBP","in_stock":true}]},{"product_id":"artificial-intelligence-fundamentals-for-business-leaders-9780645510553","title":"Artificial Intelligence Fundamentals for Business Leaders","description":"","brand":"Ines Alexandra de Castro Almeida","offers":[{"title":"Default Title","offer_id":52084025033047,"sku":"9780645510553","price":19.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"current-applications-of-deep-learning-in-cancer-diagnostics-9781032223193","title":"Current Applications of Deep Learning in Cancer Diagnostics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book examines deep learning-based approaches in the field of cancer diagnostics, as well as pre-processing techniques, which are essential to cancer diagnostics. Topics include introduction to current applications of deep learning in cancer diagnostics, pre-processing of cancer data using deep learning, review of deep learning techniques in oncology, overview of advanced deep learning techniques in cancer diagnostics, prediction of cancer susceptibility using deep learning techniques, prediction of cancer reoccurrence using deep learning techniques, deep learning techniques to predict the grading of human cancer, different human cancer detection using deep learning techniques, prediction of cancer survival using deep learning techniques, complexity in the use of deep learning in cancer diagnostics, and challenges and future scopes of deep learning techniques in oncology.\u003c\/p\u003e","brand":"CRC Press","offers":[{"title":"Default Title","offer_id":52084466581847,"sku":"9781032223193","price":42.74,"currency_code":"GBP","in_stock":true}]},{"product_id":"deep-learning-crash-course-for-beginners-with-python-theory-and-applications-of-artificial-neural-networks-cnn-rnn-lstm-and-autoencoders-using-tensorflow-2-0-contains-exercises-with-solutions-and-hands-on-projects-9781734790122","title":"Deep Learning Crash Course for Beginners with Python: Theory and Applications of Artificial Neural Networks, CNN, RNN, LSTM and Autoencoders using TensorFlow 2.0- Contains Exercises with Solutions and Hands-On Projects","description":"","brand":"AI Publishing LLC","offers":[{"title":"Default Title","offer_id":52085451915607,"sku":"9781734790122","price":13.55,"currency_code":"GBP","in_stock":true}]},{"product_id":"artificial-neural-network-applications-in-business-and-engineering-9781799832393","title":"Artificial Neural Network Applications in Business and Engineering","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIn today's modernized market, various disciplines continue to search for universally functional technologies that improve upon traditional processes. Artificial neural networks are a set of statistical modeling tools that are capable of processing nonlinear data with strong accuracy. Due to their complexity, utilizing their potential was previously seen as a challenge. However, with the development of artificial intelligence, this technology has proven to be an effective and efficient problem-solving method. \u003cbr\u003e\u003cbr\u003e\u003cem\u003eArtificial Neural Network Applications in Business and Engineering\u003c\/em\u003e is an essential reference source that illustrates recent advancements of artificial neural networks in various professional fields, accompanied by specific case studies and practical examples. Featuring research on topics such as training algorithms, transportation, and computer security, this book is ideally designed for researchers, students, developers, managers, engineers, academicians, industrialists, policymakers, and educators seeking coverage on modern trends in artificial neural networks and their real-world implementations.","brand":"IGI Global","offers":[{"title":"Default Title","offer_id":52085559099735,"sku":"9781799832393","price":182.7,"currency_code":"GBP","in_stock":true}]},{"product_id":"algorithmic-short-selling-with-python-refine-your-algorithmic-trading-edge-consistently-generate-investment-ideas-and-build-a-robust-long-short-product-9781801815192","title":"Algorithmic Short Selling with Python: Refine your algorithmic trading edge, consistently generate investment ideas, and build a robust long\/short product","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eLeverage Python source code to revolutionize your short selling strategy and to consistently make profits in bull, bear, and sideways markets\u003c\/b\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eUnderstand techniques such as trend following, mean reversion, position sizing, and risk management in a short-selling context\u003c\/li\u003e\n\u003cli\u003eImplement Python source code to explore and develop your own investment strategy\u003c\/li\u003e\n\u003cli\u003eTest your trading strategies to limit risk and increase profits\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eIf you are in the long\/short business, learning how to sell short is not a choice. Short selling is the key to raising assets under management. This book will help you demystify and hone the short selling craft, providing Python source code to construct a robust long\/short portfolio. It discusses fundamental and advanced trading concepts from the perspective of a veteran short seller.\u003c\/p\u003e \u003cp\u003eThis book will take you on a journey from an idea (“buy bullish stocks, sell bearish ones”) to becoming part of the elite club of long\/short hedge fund algorithmic traders. You'll explore key concepts such as trading psychology, trading edge, regime definition, signal processing, position sizing, risk management, and asset allocation, one obstacle at a time. Along the way, you'll will discover simple methods to consistently generate investment ideas, and consider variables that impact returns, volatility, and overall attractiveness of returns.\u003c\/p\u003e \u003cp\u003eBy the end of this book, you'll not only become familiar with some of the most sophisticated concepts in capital markets, but also have Python source code to construct a long\/short product that investors are bound to find attractive.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eDevelop the mindset required to win the infinite, complex, random game called the stock market\u003c\/li\u003e\n\u003cli\u003eDemystify short selling in order to generate alpa in bull, bear, and sideways markets\u003c\/li\u003e\n\u003cli\u003eGenerate ideas consistently on both sides of the portfolio\u003c\/li\u003e\n\u003cli\u003eImplement Python source code to engineer a statistically robust trading edge\u003c\/li\u003e\n\u003cli\u003eDevelop superior risk management habits\u003c\/li\u003e\n\u003cli\u003eBuild a long\/short product that investors will find appealing\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eThis is a book by a practitioner for practitioners. It is designed to benefit a wide range of people, including long\/short market participants, quantitative participants, proprietary traders, commodity trading advisors, retail investors (pro retailers, students, and retail quants), and long-only investors.\u003c\/p\u003e\u003cp\u003eAt least 2 years of active trading experience, intermediate-level experience of the Python programming language, and basic mathematical literacy (basic statistics and algebra) are expected.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eTable of Contents\u003col\u003e\n\u003cli\u003eThe Stock Market Game\u003c\/li\u003e\n\u003cli\u003e10 Classic Myths About Short-Selling\u003c\/li\u003e\n\u003cli\u003eTake a Walk on the Wild Short-Side\u003c\/li\u003e\n\u003cli\u003eLong\/Short Methodologies: Absolute and Relative\u003c\/li\u003e\n\u003cli\u003eRegime Definition\u003c\/li\u003e\n\u003cli\u003eThe Trading Edge is a Number, and Here is the Formula\u003c\/li\u003e\n\u003cli\u003eImprove Your Trading Edge\u003c\/li\u003e\n\u003cli\u003ePosition Sizing: Money is Made in the Money Management Module\u003c\/li\u003e\n\u003cli\u003eRisk is a number\u003c\/li\u003e\n\u003cli\u003eRefining the Investment Universe\u003c\/li\u003e\n\u003cli\u003eThe Long\/Short Toolbox\u003c\/li\u003e\n\u003cli\u003eSignals and Execution\u003c\/li\u003e\n\u003cli\u003ePortfolio Management System\u003c\/li\u003e\n\u003cli\u003eAppendix\u003c\/li\u003e\n\u003c\/ol\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":52085573091671,"sku":"9781801815192","price":47.23,"currency_code":"GBP","in_stock":true}]},{"product_id":"hands-on-graph-neural-networks-using-python-practical-techniques-and-architectures-for-building-powerful-graph-and-deep-learning-apps-with-pytorch-9781804617526","title":"Hands-On Graph Neural Networks Using Python: Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDesign robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and apps Purchase of the print or Kindle book includes a free PDF eBook  Key Features  Implement -of-the-art graph neural  architectures in Python Create your own graph datasets from tabular data Build powerful traffic forecasting, recommender systems, and anomaly detection applications  Book DescriptionGraph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as  networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery. Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you’ll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps. By the end of this book, you’ll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.What you will learn  Understand the fundamental concepts of graph neural networks Implement graph neural networks using Python and PyTorch Geometric Classify nodes, graphs, and edges using millions of samples Predict and generate realistic graph topologies Combine heterogeneous sources to improve performance Forecast future events using topological information Apply graph neural networks to solve real-world problems  Who this book is forThis book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you’re new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eTable of Contents\u003col\u003e\n\u003cli\u003eGetting Started with Graph Learning\u003c\/li\u003e\n\u003cli\u003eGraph Theory for Graph Neural Networks\u003c\/li\u003e\n\u003cli\u003eCreating Node Representations with DeepWalk\u003c\/li\u003e\n\u003cli\u003eImproving Embeddings with Biased Random Walks in Node2Vec\u003c\/li\u003e\n\u003cli\u003eIncluding Node Features with Vanilla Neural Networks\u003c\/li\u003e\n\u003cli\u003eIntroducing Graph Convolutional Networks\u003c\/li\u003e\n\u003cli\u003eGraph Attention Networks\u003c\/li\u003e\n\u003cli\u003eScaling Graph Neural Networks with GraphSAGE\u003c\/li\u003e\n\u003cli\u003eDefining Expressiveness for Graph Classification\u003c\/li\u003e\n\u003cli\u003ePredicting Links with Graph Neural Networks\u003c\/li\u003e\n\u003cli\u003eGenerating Graphs Using Graph Neural Networks\u003c\/li\u003e\n\u003cli\u003eLearning from Heterogeneous Graphs\u003c\/li\u003e\n\u003cli\u003eTemporal Graph Neural Networks\u003c\/li\u003e\n\u003cli\u003eExplaining Graph Neural Networks\u003c\/li\u003e\n\u003cli\u003eForecasting Traffic Using A3T-GCN\u003c\/li\u003e\n\u003cli\u003eDetecting Anomalies Using Heterogeneous Graph Neural Networks\u003c\/li\u003e\n\u003cli\u003eBuilding a Recommender System Using LightGCN\u003c\/li\u003e\n\u003cli\u003eUnlocking the Potential of Graph Neural Networks for Real-Word Applications\u003c\/li\u003e\n\u003c\/ol\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":52085590950231,"sku":"9781804617526","price":37.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"generative-ai-with-langchain-build-large-language-model-llm-apps-with-python-chatgpt-and-other-llms-9781835083468","title":"Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eGet to grips with the LangChain framework from theory to deployment and develop production-ready applications. Code examples regularly updated on GitHub to keep you abreast of the latest LangChain developments. Purchase of the print or Kindle book includes a free PDF eBook.  Key Features  Learn how to leverage LLMs’ capabilities and work around their inherent weaknesses Delve into the realm of LLMs with LangChain and go on an in-depth exploration of their fundamentals, ethical dimensions, and application challenges Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality  Book DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Bard. It also demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications.  Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learn  Understand LLMs, their strengths and limitations Grasp generative AI fundamentals and industry trends Create LLM apps with LangChain like question-answering systems and chatbots Understand transformer models and attention mechanisms Automate data analysis and visualization using pandas and Python Grasp prompt engineering to improve performance Fine-tune LLMs and get to know the tools to unleash their power Deploy LLMs as a service with LangChain and apply evaluation strategies Privately interact with documents using open-source LLMs to prevent data leaks  Who this book is forThe book is for developers, researchers, and anyone interested in learning more about LLMs. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs and are looking to stay ahead of the curve in the LLMs and LangChain arena.  Basic knowledge of Python is a prerequisite, while some prior exposure to machine learning will help you follow along more easily.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eTable of Contents\u003col\u003e\n\u003cli\u003eWhat Is Generative AI?\u003c\/li\u003e\n\u003cli\u003eLangChain for LLM Apps\u003c\/li\u003e\n\u003cli\u003eGetting Started with LangChain\u003c\/li\u003e\n\u003cli\u003eBuilding Capable Assistants\u003c\/li\u003e\n\u003cli\u003eBuilding a Chatbot like ChatGPT\u003c\/li\u003e\n\u003cli\u003eDeveloping Software with Generative AI\u003c\/li\u003e\n\u003cli\u003eLLMs for Data Science\u003c\/li\u003e\n\u003cli\u003eCustomizing LLMs and Their Output\u003c\/li\u003e\n\u003cli\u003eGenerative AI in Production\u003c\/li\u003e\n\u003cli\u003eThe Future of Generative Models\u003c\/li\u003e\n\u003c\/ol\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":52085603139927,"sku":"9781835083468","price":66.02,"currency_code":"GBP","in_stock":true}]},{"product_id":"machine-learning-for-algorithmic-trading-predictive-models-to-extract-signals-from-market-and-alternative-data-for-systematic-trading-strategies-with-python-2nd-edition-9781839217715","title":"Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eLeverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePurchase of the print or Kindle book includes a free eBook in the PDF format.\u003c\/b\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eDesign, train, and evaluate machine learning algorithms that underpin automated trading strategies\u003c\/li\u003e\n\u003cli\u003eCreate a research and strategy development process to apply predictive modeling to trading decisions\u003c\/li\u003e\n\u003cli\u003eLeverage NLP and deep learning to extract tradeable signals from market and alternative data\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.\u003c\/p\u003e\u003cp\u003eThis book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research.\u003c\/p\u003e\u003cp\u003eThis edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples.\u003c\/p\u003e\u003cp\u003eBy the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eLeverage market, fundamental, and alternative text and image data\u003c\/li\u003e\n\u003cli\u003eResearch and evaluate alpha factors using statistics, Alphalens, and SHAP values\u003c\/li\u003e\n\u003cli\u003eImplement machine learning techniques to solve investment and trading problems\u003c\/li\u003e\n\u003cli\u003eBacktest and evaluate trading strategies based on machine learning using Zipline and Backtrader\u003c\/li\u003e\n\u003cli\u003eOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio\u003c\/li\u003e\n\u003cli\u003eCreate a pairs trading strategy based on cointegration for US equities and ETFs\u003c\/li\u003e\n\u003cli\u003eTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes data\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies.\u003c\/p\u003e\u003cp\u003eSome understanding of Python and machine learning techniques is required.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eTable of Contents\u003col\u003e\n\u003cli\u003eMachine Learning for Trading – From Idea to Execution\u003c\/li\u003e\n\u003cli\u003eMarket and Fundamental Data – Sources and Techniques\u003c\/li\u003e\n\u003cli\u003eAlternative Data for Finance – Categories and Use Cases\u003c\/li\u003e\n\u003cli\u003eFinancial Feature Engineering – How to Research Alpha Factors\u003c\/li\u003e\n\u003cli\u003ePortfolio Optimization and Performance Evaluation\u003c\/li\u003e\n\u003cli\u003eThe Machine Learning Process\u003c\/li\u003e\n\u003cli\u003eLinear Models – From Risk Factors to Return Forecasts\u003c\/li\u003e\n\u003cli\u003eThe ML4T Workflow – From Model to Strategy Backtesting\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e(N.B. Please use the Look Inside option to see further chapters)\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ol\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":52085638103383,"sku":"9781839217715","price":43.99,"currency_code":"GBP","in_stock":true}]}],"url":"https:\/\/bookcurl.com\/collections\/neural-networks-and-fuzzy-systems.oembed?page=6","provider":"Book Curl","version":"1.0","type":"link"}