Neural networks and fuzzy systems Books

Book Synopsis Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required. Grokking 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. We 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. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. • Foundational reinforcement learning concepts and methods • The most popular deep reinforcement learning agents solving high-dimensional environments • Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior on their own from raw sensory input. 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.

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Book SynopsisMark Liu is a professor and program director known for translating cutting-edge AI into practical curricula. With years mentoring graduate students and professionals, Mark brings clarity, rigor, and enthusiasm to every page. He distills deep generative-model expertise into step-by-step guidance that empowers readers to build powerful visual AI systems. 

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Book SynopsisIt's time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily available machine learning tools! In Grokking Machine Learning, expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you. Practical examples illustrate each new concept to ensure you’re grokking as you go. You’ll build models for spam detection, language analysis, and image recognition as you lock in each carefully-selected skill. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Key Features · Different types of machine learning, including supervised and unsupervised learning · Algorithms for simplifying, classifying, and splitting data · Machine learning packages and tools · Hands-on exercises with fully-explained Python code samples For readers with intermediate programming knowledge in Python or a similar language. About the technology Machine learning is a collection of mathematically-based techniques and algorithms that enable computers to identify patterns and generate predictions from data. This revolutionary data analysis approach is behind everything from recommendation systems to self-driving cars, and is transforming industries from finance to art. Luis G. Serrano has 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.

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Book SynopsisThis 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. This 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. This 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.Table of Contents1. 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.

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Book SynopsisDeep learning from the ground up using R and the powerful Keras library! In Deep Learning with R, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Deep Learning with R, Second Edition shows you how to put deep learning into action. It's based on the revised new edition of François Chollet's bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks. about the technology Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. 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 Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation about the reader For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.

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Book SynopsisAdvancements 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.Stochastic Optimization for Large-scale Machine Learning identifies 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.Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key reTable of ContentsList of FiguresList of TablesPreface Section I BACKGROUND Introduction1.1 LARGE-SCALE MACHINE LEARNING 1.2 OPTIMIZATION PROBLEMS 1.3 LINEAR CLASSIFICATION1.3.1 Support Vector Machine (SVM) 1.3.2 Logistic Regression 1.3.3 First and Second Order Methods1.3.3.1 First Order Methods 1.3.3.2 Second Order Methods 1.4 STOCHASTIC APPROXIMATION APPROACH 1.5 COORDINATE DESCENT APPROACH 1.6 DATASETS 1.7 ORGANIZATION OF BOOK Optimisation Problem, Solvers, Challenges and Research Directions2.1 INTRODUCTION 2.1.1 Contributions 2.2 LITERATURE 2.3 PROBLEM FORMULATIONS 2.3.1 Hard Margin SVM (1992) 2.3.2 Soft Margin SVM (1995) 2.3.3 One-versus-Rest (1998) 2.3.4 One-versus-One (1999) 2.3.5 Least Squares SVM (1999) 2.3.6 v-SVM (2000) 2.3.7 Smooth SVM (2001) 2.3.8 Proximal SVM (2001) 2.3.9 Crammer Singer SVM (2002) 2.3.10 Ev-SVM (2003) 2.3.11 Twin SVM (2007) 2.3.12 Capped lp-norm SVM (2017) 2.4 PROBLEM SOLVERS 2.4.1 Exact Line Search Method 2.4.2 Backtracking Line Search 2.4.3 Constant Step Size 2.4.4 Lipschitz & Strong Convexity Constants 2.4.5 Trust Region Method 2.4.6 Gradient Descent Method 2.4.7 Newton Method 2.4.8 Gauss-Newton Method 2.4.9 Levenberg-Marquardt Method 2.4.10 Quasi-Newton Method 2.4.11 Subgradient Method 2.4.12 Conjugate Gradient Method 2.4.13 Truncated Newton Method 2.4.14 Proximal Gradient Method 2.4.15 Recent Algorithms 2.5 COMPARATIVE STUDY 2.5.1 Results from Literature 2.5.2 Results from Experimental Study 2.5.2.1 Experimental Setup and Implementation Details 2.5.2.2 Results and Discussions 2.6 CURRENT CHALLENGES AND RESEARCH DIRECTIONS 2.6.1 Big Data Challenge 2.6.2 Areas of Improvement 2.6.2.1 Problem Formulations 2.6.2.2 Problem Solvers 2.6.2.3 Problem Solving Strategies/Approaches 2.6.2.4 Platforms/Frameworks 2.6.3 Research Directions 2.6.3.1 Stochastic Approximation Algorithms 2.6.3.2 Coordinate Descent Algorithms 2.6.3.3 Proximal Algorithms 2.6.3.4 Parallel/Distributed Algorithms 2.6.3.5 Hybrid Algorithms 2.7 CONCLUSION Section II FIRST ORDER METHODSMini-batch and Block-coordinate Approach 3.1 INTRODUCTION 3.1.1 Motivation 3.1.2 Batch Block Optimization Framework (BBOF) 3.1.3 Brief Literature Review 3.1.4 Contributions 3.2 STOCHASTIC AVERAGE ADJUSTED GRADIENT (SAAG) METHODS3.3 ANALYSIS 3.4 NUMERICAL EXPERIMENTS 3.4.1 Experimental setup 3.4.2 Convergence against epochs 3.4.3 Convergence against Time 3.5 CONCLUSION AND FUTURE SCOPE Variance Reduction Methods 4.1 INTRODUCTION 4.1.1 Optimization Problem 4.1.2 Solution Techniques for Optimization Problem 4.1.3 Contributions 4.2 NOTATIONS AND RELATED WORK 4.2.1 Notations 4.2.2 Related Work 4.3 SAAG-I, II AND PROXIMAL EXTENSIONS 4.4 SAAG-III AND IV ALGORITHMS 4.5 ANALYSIS 4.6 EXPERIMENTAL RESULTS 4.6.1 Experimental Setup 4.6.2 Results with Smooth Problem 4.6.3 Results with non-smooth Problem 4.6.4 Mini-batch Block-coordinate versus mini-batch setting 4.6.5 Results with SVM 4.7 CONCLUSION Learning and Data Access 5.1 INTRODUCTION 5.1.1 Optimization Problem 5.1.2 Literature Review 5.1.3 Contributions 5.2 SYSTEMATIC SAMPLING 5.2.1 Definitions 5.2.2 Learning using Systematic Sampling 5.3 ANALYSIS 5.4 EXPERIMENTS 5.4.1 Experimental Setup 5.4.2 Implementation Details 5.4.3 Results 5.5 CONCLUSION Section III SECOND ORDER METHODS Mini-batch Block-coordinate Newton Method 6.1 INTRODUCTION 6.1.1 Contributions 6.2 MBN 6.3 EXPERIMENTS 6.3.1 Experimental Setup 6.3.2 Comparative Study 6.4 CONCLUSION Stochastic Trust Region Inexact Newton Method 7.1 INTRODUCTION 7.1.1 Optimization Problem 7.1.2 Solution Techniques 7.1.3 Contributions 7.2 LITERATURE REVIEW 7.3 TRUST REGION INEXACT NEWTON METHOD 7.3.1 Inexact Newton Method 7.3.2 Trust Region Inexact Newton Method 7.4 STRON 7.4.1 Complexity 7.4.2 Analysis 7.5 EXPERIMENTAL RESULTS 7.5.1 Experimental Setup 7.5.2 Comparative Study 7.5.3 Results with SVM 7.6 EXTENSIONS 7.6.1 PCG Subproblem Solver 17.6.2 Stochastic Variance Reduced Trust Region Inexact Newton Method 7.7 CONCLUSION Section IV CONCLUSIONConclusion and Future Scope 8.1 FUTURE SCOPE 142 Bibliography Index

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Book SynopsisMoving well beyond simply speeding up computation, this book tackles AI for Finance from a range of perspectives including business, technology, research, and students. Covering aspects like algorithms, big data, and machine learning, this book answers these and many other questions.Trade Review“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.” --Michael Dempster, ‎Professor Emeritus in the Statistical Laboratory at the University of Cambridge“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.” --Amadeo Alentorn, PhD, Head of Systematic Equities at Jupiter Asset Management"AI for Finance 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." --Richard Olsen, Founder and CEO of Lykke, co-founder of OANDA, and pioneer in high frequency finance and fintech“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!” --Dr David Norman, Founder of the TTC Institute"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." -- Dr Ali Rais Shaghaghi, Lead Data Scientist at NatWest Group.Table of Contents1. 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

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Book SynopsisThis modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.Table of ContentsAcknowledgements. 1. Introduction. Part I. Hopfield Networks: 2. Deterministic Hopfield networks; 3. Stochastic Hopfield networks; 4. The Boltzmann distribution. Part II. Supervised Learning: 5. Perceptrons; 6. Stochastic gradient descent; 7. Deep learning; 8. Convolutional networks; 9. Supervised recurrent networks. Part III. Learning Without Labels: 10. Unsupervised learning; 11. Reinforcement learning. Bibliography. Author Index. Index.

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Book SynopsisDiscover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R librariesKey Features Implement deep learning algorithms to build AI models with the help of tips and tricks Understand how deep learning models operate using expert techniques Apply reinforcement learning, computer vision, GANs, and NLP using a range of datasets Book DescriptionDeep 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.This 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. By 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.What you will learn Learn how to create binary and multi-class deep neural network models Implement GANs for generating new images Create autoencoder neural networks for image dimension reduction, image de-noising and image correction Implement deep neural networks for performing efficient text classification Learn to define a recurrent convolutional network model for classification in Keras Explore best practices and tips for performance optimization of various deep learning models Who this book is forThis 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. A solid understanding of machine learning and working knowledge of the R programming language are required.Table of ContentsTable of Contents Revisiting Deep Learning architecture and techniques Deep Neural Networks for multiclass classification Deep Neural Networks for regression Image classification and recognition Image classification using convolutional neural networks Applying Autoencoder neural networks using Keras Image classification for small data using transfer learning Creating new images using generative adversarial networks Deep network for text classification Text classification using recurrent neural networks Text classification using Long Short-Term Memory Network Text classification using convolutional recurrent networks Tips, tricks and the road ahead

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Book SynopsisCreate and unleash the power of neural networks by implementing C# and .Net codeKey Features Get a strong foundation of neural networks with access to various machine learning and deep learning libraries Real-world case studies illustrating various neural network techniques and architectures used by practitioners Cutting-edge coverage of Deep Networks, optimization algorithms, convolutional networks, autoencoders and many more Book DescriptionNeural networks have made a surprise comeback in the last few years and have brought tremendous innovation in the world of artificial intelligence. The goal of this book is to provide C# programmers with practical guidance in solving complex computational challenges using neural networks and C# libraries such as CNTK, and TensorFlowSharp. 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.This 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.Throughout this book, you will be working on interesting demonstrations that will make it easier to implement complex neural networks in your enterprise applications.What you will learn Understand perceptrons and how to implement them in C# Learn how to train and visualize a neural network using cognitive services Perform image recognition for detecting and labeling objects using C# and TensorFlowSharp Detect specific image characteristics such as a face using Accord.Net Demonstrate particle swarm optimization using a simple XOR problem and Encog Train convolutional neural networks using ConvNetSharp Find optimal parameters for your neural network functions using numeric and heuristic optimization techniques. Who this book is forThis 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 bookTable of ContentsTable of Contents A Quick Refresher Building our first Neural Network Together Decision Tress and Random Forests Face and Motion Detection Training CNNs using ConvNetSharp Training Autoencoders Using RNNSharp Replacing Back Propagation with PSO Function Optimizations; How and Why Finding Optimal Parameters Object Detection with TensorFlowSharp Time Series Prediction and LSTM Using CNTK GRUs Compared to LSTMs, RNNs, and Feedforward Networks Appendix A- Activation Function Timings Appendix B- Function Optimization Reference

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Book SynopsisA comprehensive guide to developing neural network-based solutions using TensorFlow 2.0Key Features Understand the basics of machine learning and discover the power of neural networks and deep learning Explore the structure of the TensorFlow framework and understand how to transition to TF 2.0 Solve any deep learning problem by developing neural network-based solutions using TF 2.0 Book DescriptionTensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers.This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub.By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.What you will learn Grasp machine learning and neural network techniques to solve challenging tasks Apply the new features of TF 2.0 to speed up development Use TensorFlow Datasets (tfds) and the tf.data API to build high-efficiency data input pipelines Perform transfer learning and fine-tuning with TensorFlow Hub Define and train networks to solve object detection and semantic segmentation problems Train Generative Adversarial Networks (GANs) to generate images and data distributions Use the SavedModel file format to put a model, or a generic computational graph, into production Who this book is forIf you're a developer who wants to get started with machine learning and TensorFlow, or a data scientist interested in developing neural network solutions in TF 2.0, this book is for you. Experienced machine learning engineers who want to master the new features of the TensorFlow framework will also find this book useful.Basic knowledge of calculus and a strong understanding of Python programming will help you grasp the topics covered in this book.Table of ContentsTable of Contents What is Machine Learning? Neural Networks and Deep Learning TensorFlow Graph Architecture TensorFlow 2.0 Architecture Efficient Data Input Pipelines and Estimator API Image Classification using TensorFlow Hub Introduction to Object Detection Semantic Segmentation and Custom Dataset Builder Generative Adversarial Networks Bringing a Model to Production

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Book SynopsisStart 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 guideKey Features Use TensorFlow to write reinforcement learning agents for performing challenging tasks Learn how to solve finite Markov decision problems Train models to understand popular video games like Breakout Book DescriptionVarious 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.Starting 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.By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.What you will learn Use OpenAI Gym as a framework to implement RL environments Find out how to define and implement reward function Explore Markov chain, Markov decision process, and the Bellman equation Distinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning Understand the multi-armed bandit problem and explore various strategies to solve it Build a deep Q model network for playing the video game Breakout Who this book is forIf 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.Table of ContentsTable of Contents Introduction to Reinforcement Learning Markov Decision Processes and Bellman Equations Deep Learning in Practice with TensorFlow 2 Getting Started with OpenAI and TensorFlow for Reinforcement Learning Dynamic Programming Monte Carlo Methods Temporal Difference Learning The Multi-Armed Bandit Problem What Is Deep Q Learning? Playing an Atari Game with Deep Recurrent Q Networks Policy-Based Methods for Reinforcement Learning Evolutionary Strategies for RL

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Book SynopsisNanoscale devices attracted significant research effort from the industry and academia due to their operation principals being based on different physical properties which provide advantages in the design of certain classes of circuits over conventional CMOS transistors. Neuromorphic Circuits for Nanoscale Devices contains recent research papers presented in various international conferences and journals to provide insight into how the operational principles of the nanoscale devices can be utilized for the design of neuromorphic circuits for various applications of non-volatile memory, neural network training/learning, and image processing. The topics discussed in the book include:Nanoscale Crossbar Memory DesignQ-Learning and Value Iteration using Nanoscale DevicesImage Processing and Computer Vision Applications for Nanoscale DevicesNanoscale Devices based Cellular Nonlinear/Neural NetworksTrade Review"This should be a very appealing book: the authors are very good, and the topics are very current." -Dinesh Kant Kumar, RMIT, Melbourne, Australia -- Dinesh Kant KumarTable of ContentsNeuromorphic Circuits for Nanoscale Devices

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Book SynopsisGain a new perspective on how the brain works and inspires new avenues for design in computer science and engineering This unique book is the first of its kind to introduce human memory and basic cognition in terms of physical circuits, beginning with the possibilities of ferroelectric behavior of neural membranes, moving to the logical properties of neural pulses recognized as solitons, and finally exploring the architecture of cognition itself. It encourages invention via the methodical study of brain theory, including electrically reversible neurons, neural networks, associative memory systems within the brain, neural state machines within associative memory, and reversible computers in general. These models use standard analog and digital circuits that, in contrast to models that include non-physical components, may be applied directly toward the goal of constructing a machine with artificial intelligence based on patterns of the brain. Writing from the circuits aTable of ContentsPREFACE. 1 BRAIN BEHAVIOR POINTS THE WAY. Introduction. Modeling. Uses of models. Why Thinking Dissipates So Few Calories? The Miracle of Parallel Processing. Singularity. The Benefits of Reading This Book. Overview of the Book. Applications of the Models in the Book. Conclusions. Exercises. 2 NEURAL MEMBRANES AND ANIMAL ELECTRICITY. Introduction. The Physical Neuron. Ionic Solutions and Stray Electrons. Nernst Voltage. Ion-Channel Model. Applications. Conclusions. Exercises. 3 NEURAL PULSES AND NEURAL MEMORY. Introduction. Derivation of a Neural Pulse Using Basic Physics. Neuron Signal Propagation. Modeling Neurons as Adiabatic. Neurons for Memory. Applications. Conclusions. Exercises. Appendix: Asymptotically Adiabatic Circuits. 4 CIRCUITS AND SYSTEMS FOR MEMORIZATION AND RECALL. Introduction. Psychological Considerations When Modeling Human Memory. Basic Assumptions to Create A Model. Short-Term Memory and Consciousness. Cognitive Architecture. Discussion of the Model. Enable Neural Logic. Models for Memorization. Applications. Conclusions. Exercises. 5 DENDRITIC PROCESSING AND HUMAN LEARNING. Introduction. Biological Versus Artificial Neural Networks. Dendrites Neurons for Combinational Learning. Neurons for State-Machine Learning. Learning Circuits. Dendritic Processing Models. Enabled Logic Directly at the Soma. Comments on the Adiabatic Nature of Dendrites. Applications. Conclusions. Exercises. Appendix: Circuit Simulations of Neural Soliton Propagation. Conclusions. 6 ARTIFICIAL LEARNING IN ARTIFICIAL NEURAL NETWORKS. Introduction. Artificial Neurons. Artificial Learning Methods. Discussion of Learning Methods. Conclusion. Exercises. 7 THE ASSET OF REVERSIBILITY IN HUMANS AND MACHINES. Introduction. Savants. Neural Models that Explain Savants. Parallel Processing and the Savant Brain. Computational Possibilities Using Conditional Toggle Memory. The Cost of Computation. Reversible Programming. Conclusions. Exercises. Appendix: Split-Level Charge Recovery Logic. 8 ELECTRICALLY REVERSIBLE NANOPROCESSORS. Introduction. A Gauge for Classical Parallelism. Design Rules for Electrical Reversibility. Reversible System Architecture. Architecture for Self-Analyzing Memory Words. Electrically Reversible Toggle Circuit. Reversible Addition Programming Example. Reversible Subtraction Programming Example. Conclusions. Exercises. 9 MULTIPLICATION, DIVISION, AND HAMILTONIAN CIRCUITS. Introduction. Unsigned Multiplication. Restoring Division. Solving Hard Problems. Hamiltonian Circuits. The Initialization of Toggle Memory in Nanoprocessors. Logically Reversible Programming Using Nanobrains. Conclusions. Exercises. 10 QUANTUM VERSUS CLASSICAL COMPUTING. Introduction. Physical Qubits. Quantum Boolean Functions. Quantum Computer Programming. Historical Quantum Computing Algorithms. Conclusions. Exercises. APPENDIX A HUMAN BRAIN ANATOMY. Components of a Brain. Forebrain Structure. APPENDIX B THE PSYCHOLOGICAL SCIENCE OF MEMORY. Short-Term Memory. Long-Term Memory. Studies in Learning. Memory Retrieval. Serial Reproduction. Memory Theorists. Dreams. APPENDIX C BRAIN SCANNING. Electroencephalography. Magnetic Resonance Imaging. Functional Magnetic Resonance Imaging. Positron Emission Tomography. Computerized Axial Tomography. APPENDIX D BIOGRAPHIES OF PERSONS OF SCIENTIFIC INTEREST. FOR FURTHER STUDY. INDEX.

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Book SynopsisLike 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. The 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.Table of ContentsChapter 1 Data Fitting with Linear Models 1 Chapter 2 Pattern Recognition 68 Chapter 3 Multilayer Perceptrons 100 Chapter 4 Designing and Training MLPS 173 Chapter 5 Function Approximation with MLPs, Radial Basis Functions, and Support Vector Machines 223 Chapter 6 Hebbian Learning and Principal Component Analysis 279 Chapter 7 Competitive and Kohonen Networks 333 Chapter 8 Principles of Digital Signal Processing 364 Chapter 9 Adaptive Filters 429 Chapter 10 Temporal Processing with Neural Networks 473 Chapter 11 Training and Using Recurrent Networks 525 Appendix A Elements of Linear Algebra and Pattern Recognition 589 Appendix B NeuroSolutions Tutorial 613 Appendix C Data Directory 637 Glossary 639 Index 647

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Book SynopsisNow, for the first time, publication of the landmark work inbackpropagation! Scientists, engineers, statisticians, operationsresearchers, and other investigators involved in neural networkshave long sought direct access to Paul Werbos''s groundbreaking,much-cited 1974 Harvard doctoral thesis, The Roots ofBackpropagation, which laid the foundation of backpropagation. Now,with the publication of its full text, these practitioners can gostraight to the original material and gain a deeper, practicalunderstanding of this unique mathematical approach to socialstudies and related fields. In addition, Werbos has provided threemore recent research papers, which were inspired by his originalwork, and a new guide to the field. Originally written for readerswho lacked any knowledge of neural nets, The Roots ofBackpropagation firmly established both its historical andcontinuing significance as it: * Demonstrates the ongoing value and new potential ofbackpropagation * Creates a wealth of souTable of ContentsTHESIS. Beyond Regression: New Tools for Prediction and Analysis in theBehavioral Sciences. Dynamic Feedback, Statistical Estimation, and Systems Optimization:General Techniques. The Multivariate ARMA(1,1) Model: Its Significance andEstimation. Simulation Studies of Techniques of Time-Series Analysis. General Applications of These Ideas: Practical Hazards and NewPossibilities. Nationalism and Social Communications: A Test Case for MathematicalApproaches. APPLICATIONS AND EXTENSIONS. Forms of Backpropagation for Sensitivity Analysis, Optimization,and Neural Networks. Backpropagation Through Time: What It Does and How to Do It. Neurocontrol: Where It Is Going and Why It Is Crucial. Neural Networks and the Human Mind: New Mathematics Fits HumanisticInsight. Index.

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Book SynopsisConnectionism and the Mind 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. Read two of the sample chapters on line: Connectionism and the Dynamical Approach to Cognition: http://www.blackwellpublishing.com/pdf/bechtel.pdf Networks, Robots, and Artificial Life: http://www.blackwellpublishing.com/pdf/bechtel2.pdfTrade Review"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." Andy Clark, University of Sussex "Connectionism and the Mind 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." Jeff Elman, University of California at San DiegoTable of ContentsPreface xiii 1 Networks Versus Symbol Systems: Two Approaches To Modeling Cognition 1 1.1 A Revolution in the Making? 1 1.2 Forerunners of Connectionism: Pandemonium and Perceptrons 2 1.3 The Allure of Symbol Manipulation 7 1.3.1 From logic to artificial intelligence 7 1.3.2 From linguistics to information processing 10 1.3.3 Using artificial intelligence to simulate human information processing 11 1.4 The Decline and Re-emergence of Network Models 12 1.4.1 Problems with perceptrons 12 1.4.2 Re-emergence: The new connectionism 13 1.5 New Alliances and Unfinished Business 15 Notes 17 Sources and Suggested Readings 17 2 Connectionist Architectures 19 2.1 The Flavor of Connectionist Processing: A Simulation of Memory Retrieval 19 2.1.1 Components of the model 20 2.1.2 Dynamics of the model 22 2.1.2.1 Memory retrieval in the Jets and Sharks network 22 2.1.2.2 The equations 23 2.1.3 Illustrations of the dynamics of the model 24 2.1.3.1 Retrieving properties from a name 24 2.1.3.2 Retrieving a name from other properties 26 2.1.3.3 Categorization and prototype formation 26 2.1.3.4 Utilizing regularities 28 2.2 The Design Features of a Connectionist Architecture 29 2.2.1 Patterns of connectivity 29 2.2.1.1 Feedforward networks 29 2.2.1.2 Interactive networks 31 2.2.2 Activation rules for units 32 2.2.2.1 Feedforward networks 32 2.2.2.2 Interactive networks: Hopfield networks and Boltzmann machines 34 2.2.2.3 Spreading activation vs. interactive connectionist models 37 2.2.3 Learning principles 38 2.2.4 Semantic interpretation of connectionist systems 40 2.2.4.1 Localist networks 41 2.2.4.2 Distributed networks 41 2.3 The Allure of the Connectionist Approach 45 2.3.1 Neural plausibility 45 2.3.2 Satisfaction of soft constraints 46 2.3.3 Graceful degradation 48 2.3.4 Content-addressable memory 49 2.3.5 Capacity to learn from experience and generalize 51 2.4 Challenges Facing Connectionist Networks 51 2.5 Summary 52 Notes 52 Sources and Recommended Readings 53 3 Learning 54 3.1 Traditional and Contemporary Approaches to Learning 54 3.1.1 Empiricism 54 3.1.2 Rationalism 55 3.1.3 Contemporary cognitive science 56 3.2 Connectionist Models of Learning 57 3.2.1 Learning procedures for two-layer feedforward networks 58 3.2.1.1 Training and testing a network 58 3.2.1.2 The Hebbian rule 58 3.2.1.3 The delta rule 60 3.2.1.4 Comparing the Hebbian and delta rules 67 3.2.1.5 Limitations of the delta rule: The XOR problem 67 3.2.2 The backpropagation learning procedure for multi-layered networks 69 3.2.2.1 Introducing hidden units and backpropagation learning 69 3.2.2.2 Using backpropagation to solve the XOR problem 74 3.2.2.3 Using backpropagation to train a network to pronounce words 77 3.2.2.4 Some drawbacks of using backpropagation 78 3.2.3 Boltzmann learning procedures for non-layered networks 79 3.2.4 Competitive learning 80 3.2.5 Reinforcement learning 81 3.3 Some Issues Regarding Learning 82 3.3.1 Are connectionist systems associationist? 82 3.3.2 Possible roles for innate knowledge 84 3.3.2.1 Networks and the rationalist–empiricist continuum 84 3.3.2.2 Rethinking innateness: Connectionism and emergence 85 Notes 87 Sources and Suggested Readings 88 4 Pattern Recognition and Cognition 89 4.1 Networks as Pattern Recognition Devices 90 4.1.1 Pattern recognition in two-layer networks 90 4.1.2 Pattern recognition in multi-layered networks 93 4.1.2.1 McClelland and Rumelhart’s interactive activation model of word recognition 93 4.1.2.2 Evaluating the interactive activation model of word recognition 100 4.1.3 Generalization and similarity 101 4.2 Extending Pattern Recognition to Higher Cognition 102 4.2.1 Smolensky’s proposal: Reasoning in harmony networks 103 4.2.2 Margolis’s proposal: Cognition as sequential pattern recognition 103 4.3 Logical Inference as Pattern Recognition 106 4.3.1 What is it to learn logic? 106 4.3.2 A network for evaluating validity of arguments 109 4.3.3 Analyzing how a network evaluates arguments 112 4.3.4 A network for constructing derivations 115 4.4 Beyond Pattern Recognition 117 Notes 118 Sources and Suggested Readings 119 5 Are Rules Required to Process Representations? 120 5.1 Is Language Use Governed by Rules? 120 5.2 Rumelhart and McClelland’s Model of Past-tense Acquisition 122 5.2.1 A pattern associator with Wickelfeature encodings 122 5.2.2 Activation function and learning procedure 126 5.2.3 Overregularization in a simpler network: The rule of 78 127 5.2.4 Modeling U-shaped learning 130 5.2.5 Modeling differences between different verb classes 133 5.3Pinker and Prince’s Arguments for Rules 135 5.3.1 Overview of the critique of Rumelhart and McClelland’s model 135 5.3.2 Putative linguistic inadequacies 136 5.3.3 Putative behavioral inadequacies 139 5.3.4 Do the inadequacies reflect inherent limitations of PDP networks? 140 5.4 Accounting for the U-shaped Learning Function 141 5.4.1 The role of input for children 142 5.4.2 The role of input for networks: The rule of 78 revisited 146 5.4.3 Plunkett and Marchman’s simulations of past-tense acquisition 148 5.5 Conclusion 152 Notes 153 Sources and Suggested Readings 155 6 Are Syntactically Structured Representations Needed? 156 6.1 Fodor and Pylyshyn’s Critique: The Need for Symbolic Representations with Constituent Structure 156 6.1.1 The need for compositional syntax and semantics 156 6.1.2 Connectionist representations lack compositionality 158 6.1.3 Connectionism as providing mere implementation 160 6.2 First Connectionist Response: Explicitly Implementing Rules and Representations 163 6.2.1 Implementing a production system in a network 163 6.2.2 The variable binding problem 165 6.2.3 Shastri and Ajjanagadde’s connectionist model of variable binding 166 6.3Second Connectionist Response: Implementing Functionally Compositional Representations 170 6.3.1 Functional vs. concatenative compositionality 170 6.3.2 Developing compressed representations using Pollack’s RAAM networks 171 6.3.3 Functional compositionality of compressed representations 175 6.3.4 Performing operations on compressed representations 177 6.4 Third Connectionist Response: Employing Procedural Knowledge with External Symbols 178 6.4.1 Temporal dependencies in processing language 179 6.4.2 Achieving short-term memory with simple recurrent networks 180 6.4.3 Elman’s first study: Learning grammatical categories 181 6.4.4 Elman’s second study: Respecting dependency relations 184 6.4.5 Christiansen’s extension: Pushing the limits of SRNs 187 6.5 Using External Symbols to Provide Exact Symbol Processing 190 6.6 Clarifying the Standard: Systematicity and Degree of Generalizability 194 6.7 Conclusion 197 Notes 198 Sources and Suggested Readings 199 7 Simulating Higher Cognition: a Modular Architecture For Processing Scripts 200 7.1 Overview of Scripts 200 7.2 Overview of Miikkulainen’s DISCERN System 201 7.3Modular Connectionist Architectures 203 7.4 FGREP: An Architecture that Allows the System to Devise Its Own Representations 206 7.4.1 Why FGREP? 206 7.4.2 Exploring FGREP in a simple sentence parser 208 7.4.3 Exploring representations for words in categories 210 7.4.4 Moving to multiple modules: The DISCERN system 212 7.5 A Self-organizing Lexicon Using Kohonen Feature Maps 212 7.5.1 Innovations in lexical design 212 7.5.2 Using Kohonen feature maps in DISCERN’s lexicon 213 7.5.2.1 Orthography: From high-dimensional vector representations to map units 213 7.5.2.2 Associative connections: From the orthographic map to the semantic map 216 7.5.2.3 Semantics: From map unit to high-dimensional vector representations 216 7.5.2.4 Reversing direction: From semantic to orthographic representations 216 7.5.3 Advantages of Kohonen feature maps 216 7.6 Encoding and Decoding Stories as Scripts 217 7.6.1 Using recurrent FGREP modules in DISCERN 217 7.6.2 Using the Sentence Parser and Story Parser to encode stories 218 7.6.3 Using the Story Generator and Sentence Generator to paraphrase stories 221 7.6.4 Using the Cue Former and Answer Producer to answer questions 223 7.7 A Connectionist Episodic Memory 223 7.7.1 Making Kohonen feature maps hierarchical 223 7.7.2 How role-binding maps become self-organized 225 7.7.3 How role-binding maps become trace feature maps 225 7.8 Performance: Paraphrasing Stories and Answering Questions 228 7.8.1 Training and testing DISCERN 228 7.8.2 Watching DISCERN paraphrase a story 229 7.8.3 Watching DISCERN answer questions 229 7.9 Evaluating DISCERN 231 7.10 Paths Beyond the First Decade of Connectionism 233 Notes 234 Sources and Suggested Readings 234 8 Connectionism and the Dynamical Approach to Cognition 235 8.1 Are We on the Road to a Dynamical Revolution? 235 8.2 Basic Concepts of DST: The Geometry of Change 237 8.2.1 Trajectories in state space: Predators and prey 237 8.2.2 Bifurcation diagrams and chaos 240 8.2.3 Embodied networks as coupled dynamical systems 242 8.3Using Dynamical Systems Tools to Analyze Networks 243 8.3.1 Discovering limit cycles in network controllers for robotic insects 244 8.3.2 Discovering multiple attractors in network models of reading 246 8.3.2.1 Modeling the semantic pathway 248 8.3.2.2 Modeling the phonological pathway 249 8.3.3 Discovering trajectories in SRNs for sentence processing 253 8.3.4 Dynamical analyses of learning in networks 256 8.4 Putting Chaos to Work in Networks 257 8.4.1 Skarda and Freeman’s model of the olfactory bulb 257 8.4.2 Shifting interpretations of ambiguous displays 260 8.5 Is Dynamicism a Competitor to Connectionism? 264 8.5.1 Van Gelder and Port’s critique of classic connectionism 264 8.5.2 Two styles of modeling 265 8.5.3 Mechanistic versus covering-law explanations 266 8.5.4 Representations: Who needs them? 270 8.6 Is Dynamicism Complementary to Connectionism? 276 8.7 Conclusion 280 Notes 280 Sources and Suggested Readings 281 9 Networks, Robots, and Artificial Life 282 9.1 Robots and the Genetic Algorithm 282 9.1.1 The robot as an artificial lifeform 282 9.1.2 The genetic algorithm for simulated evolution 283 9.2 Cellular Automata and the Synthetic Strategy 284 9.2.1 Langton’s vision: The synthetic strategy 284 9.2.2 Emergent structures from simple beings: Cellular automata 286 9.2.3 Wolfram’s four classes of cellular automata 288 9.2.4 Langton and l at the edge of chaos 289 9.3Evolution and Learning in Food-seekers 291 9.3.1 Overview and study 1: Evolution without learning 291 9.3.2 The Baldwin effect and study 2: Evolution with learning 293 9.4 Evolution and Development in Khepera 295 9.4.1 Introducing Khepera 295 9.4.2 The development of phenotypes from genotypes 296 9.4.3 The evolution of genotypes 298 9.4.4 Embodied networks: Controlling real robots 298 9.5 The Computational Neuroethology of Robots 300 9.6 When Philosophers Encounter Robots 301 9.6.1 No Cartesian split in embodied agents? 301 9.6.2 No representations in subsumption architectures? 302 9.6.3 No intentionality in robots and Chinese rooms? 303 9.6.4 No armchair when Dennett does philosophy? 304 9.7 Conclusion 305 Sources and Suggested Readings 305 10 Connectionism and the Brain 306 10.1 Connectionism Meets Cognitive Neuroscience 306 10.2 Four Connectionist Models of Brain Processes 309 10.2.1 What/Where streams in visual processing 309 10.2.2 The role of the hippocampus in memory 313 10.2.2.1 The basic design and functions of the hippocampal system 313 10.2.2.2 Spatial navigation in rats 315 10.2.2.3 Spatial versus declarative memory accounts 316 10.2.2.4 Declarative memory in humans and monkeys 318 10.2.3 Simulating dyslexia in network models of reading 323 10.2.3.1 Double dissociations in dyslexia 323 10.2.3.2 Modeling deep dyslexia 327 10.2.3.3 Modeling surface dyslexia 331 10.2.3.4 Two pathways versus dual routes 335 10.2.4 The computational power of modular structure in neocortex 338 10.3The Neural Implausibility of Many Connectionist Models 341 10.3.1 Biologically implausible aspects of connectionist networks 342 10.3.2 How important is neurophysiological plausibility? 343 10.4 Whither Connectionism? 346 Notes 347 Sources and Suggested Readings 348 Appendix A: Notation 349 Appendix B: Glossary 350 Bibliography 363 Name Index 384 Subject Index 395

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Book SynopsisFUZZY COMPUTING IN DATA SCIENCE This book comprehensively explains how to use various fuzzy-based models to solve real-time industrial challenges. The 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. Audience Researchers and students in computer science, artificial intelligence, machine learning, big data analytics, and information and communication technology.Table of ContentsPreface xvii Acknowledgement xxi 1 Band Reduction of HSI Segmentation Using FCM 1 V. Saravana Kumar, S. Anantha Sivaprakasam, E.R. Naganathan, Sunil Bhutada and M. Kavitha 1.1 Introduction 2 1.2 Existing Method 3 1.2.1 K-Means Clustering Method 3 1.2.2 Fuzzy C-Means 3 1.2.3 Davies Bouldin Index 4 1.2.4 Data Set Description of HSI 4 1.3 Proposed Method 5 1.3.1 Hyperspectral Image Segmentation Using Enhanced Estimation of Centroid 5 1.3.2 Band Reduction Using K-Means Algorithm 6 1.3.3 Band Reduction Using Fuzzy C-Means 7 1.4 Experimental Results 8 1.4.1 DB Index Graph 8 1.4.2 K-Means–Based PSC (EEOC) 9 1.4.3 Fuzzy C-Means–Based PSC (EEOC) 10 1.5 Analysis of Results 12 1.6 Conclusions 16 References 17 2 A Fuzzy Approach to Face Mask Detection 21 Vatsal Mishra, Tavish Awasthi, Subham Kashyap, Minerva Brahma, Monideepa Roy and Sujoy Datta 2.1 Introduction 22 2.2 Existing Work 23 2.3 The Proposed Framework 26 2.4 Set-Up and Libraries Used 26 2.5 Implementation 27 2.6 Results and Analysis 29 2.7 Conclusion and Future Work 33 References 34 3 Application of Fuzzy Logic to the Healthcare Industry 37 Biswajeet Sahu, Lokanath Sarangi, Abhinadita Ghosh and Hemanta Kumar Palo 3.1 Introduction 38 3.2 Background 41 3.3 Fuzzy Logic 42 3.4 Fuzzy Logic in Healthcare 45 3.5 Conclusions 49 References 50 4 A Bibliometric Approach and Systematic Exploration of Global Research Activity on Fuzzy Logic in Scopus Database 55 Sugyanta Priyadarshini and Nisrutha Dulla 4.1 Introduction 56 4.2 Data Extraction and Interpretation 58 4.3 Results and Discussion 59 4.3.1 Per Year Publication and Citation Count 59 4.3.2 Prominent Affiliations Contributing Toward Fuzzy Logic 60 4.3.3 Top Journals Emerging in Fuzzy Logic in Major Subject Areas 61 4.3.4 Major Contributing Countries Toward Fuzzy Research Articles 63 4.3.5 Prominent Authors Contribution Toward the Fuzzy Logic Analysis 66 4.3.6 Coauthorship of Authors 67 4.3.7 Cocitation Analysis of Cited Authors 68 4.3.8 Cooccurrence of Author Keywords 68 4.4 Bibliographic Coupling of Documents, Sources, Authors, and Countries 70 4.4.1 Bibliographic Coupling of Documents 70 4.4.2 Bibliographic Coupling of Sources 71 4.4.3 Bibliographic Coupling of Authors 72 4.4.4 Bibliographic Coupling of Countries 73 4.5 Conclusion 74 References 76 5 Fuzzy Decision Making in Predictive Analytics and Resource Scheduling 79 Rekha A. Kulkarni, Suhas H. Patil and Bithika Bishesh 5.1 Introduction 80 5.2 History of Fuzzy Logic and Its Applications 81 5.3 Approximate Reasoning 82 5.4 Fuzzy Sets vs Classical Sets 83 5.5 Fuzzy Inference System 84 5.5.1 Characteristics of FIS 85 5.5.2 Working of FIS 85 5.5.3 Methods of FIS 86 5.6 Fuzzy Decision Trees 86 5.6.1 Characteristics of Decision Trees 87 5.6.2 Construction of Fuzzy Decision Trees 87 5.7 Fuzzy Logic as Applied to Resource Scheduling in a Cloud Environment 88 5.8 Conclusion 90 References 91 6 Application of Fuzzy Logic and Machine Learning Concept in Sales Data Forecasting Decision Analytics Using ARIMA Model 93 S. Mala and V. Umadevi 6.1 Introduction 94 6.1.1 Aim and Scope 94 6.1.2 R-Tool 94 6.1.3 Application of Fuzzy Logic 94 6.1.4 Dataset 95 6.2 Model Study 96 6.2.1 Introduction to Machine Learning Method 96 6.2.2 Time Series Analysis 96 6.2.3 Components of a Time Series 97 6.2.4 Concepts of Stationary 99 6.2.5 Model Parsimony 100 6.3 Methodology 100 6.3.1 Exploratory Data Analysis 100 6.3.1.1 Seed Types—Analysis 101 6.3.1.2 Comparison of Location and Seeds 101 6.3.1.3 Comparison of Season (Month) and Seeds 103 6.3.2 Forecasting 103 6.3.2.1 Auto Regressive Integrated Moving Average (ARIMA) 103 6.3.2.2 Data Visualization 106 6.3.2.3 Implementation Model 108 6.4 Result Analysis 108 6.5 Conclusion 110 References 110 7 Modified m-Polar Fuzzy Set ELECTRE-I Approach 113 Madan Jagtap, Prasad Karande and Pravin Patil 7.1 Introduction 114 7.1.1 Objectives 114 7.2 Implementation of m-Polar Fuzzy ELECTRE-I Integrated Shannon’s Entropy Weight Calculations 115 7.2.1 The m-Polar Fuzzy ELECTRE-I Integrated Shannon’s Entropy Weight Calculation Method 115 7.3 Application to Industrial Problems 118 7.3.1 Cutting Fluid Selection Problem 118 7.3.2 Results Obtained From m-Polar Fuzzy ELECTRE-I for Cutting Fluid Selection Problem 122 7.3.3 FMS Selection Problem 125 7.3.4 Results Obtained From m-Polar Fuzzy ELECTRE-I for FMS Selection 130 7.4 Conclusions 143 References 143 8 Fuzzy Decision Making: Concept and Models 147 Bithika Bishesh 8.1 Introduction 148 8.2 Classical Set 149 8.3 Fuzzy Set 150 8.4 Properties of Fuzzy Set 151 8.5 Types of Decision Making 153 8.5.1 Individual Decision Making 153 8.5.2 Multiperson Decision Making 157 8.5.3 Multistage Decision Making 158 8.5.4 Multicriteria Decision Making 160 8.6 Methods of Multiattribute Decision Making (MADM) 162 8.6.1 Weighted Sum Method (WSM) 162 8.6.2 Weighted Product Method (WPM) 162 8.6.3 Weighted Aggregates Sum Product Assessment (WASPAS) 163 8.6.4 Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) 166 8.7 Applications of Fuzzy Logic 167 8.8 Conclusion 169 References 169 9 Use of Fuzzy Logic for Psychological Support to Migrant Workers of Southern Odisha (India) 173 Sanjaya Kumar Sahoo and Sukanta Chandra Swain 9.1 Introduction 174 9.2 Objectives and Methodology 175 9.2.1 Objectives 175 9.2.2 Methodology 176 9.3 Effect of COVID-19 on the Psychology and Emotion of Repatriated Migrants 176 9.3.1 Psychological Variables Identified 176 9.3.2 Fuzzy Logic for Solace to Migrants 176 9.4 Findings 178 9.5 Way Out for Strengthening the Psychological Strength of the Migrant Workers through Technological Aid 178 9.6 Conclusion 179 References 180 10 Fuzzy-Based Edge AI Approach: Smart Transformation of Healthcare for a Better Tomorrow 181 B. RaviKrishna, Sirisha Potluri, J. Rethna Virgil Jeny, Guna Sekhar Sajja and Katta Subba Rao 10.1 Significance of Machine Learning in Healthcare 182 10.2 Cloud-Based Artificial Intelligent Secure Models 183 10.3 Applications and Usage of Machine Learning in Healthcare 183 10.3.1 Detecting Diseases and Diagnosis 183 10.3.2 Drug Detection and Manufacturing 183 10.3.3 Medical Imaging Analysis and Diagnosis 184 10.3.4 Personalized/Adapted Medicine 185 10.3.5 Behavioral Modification 185 10.3.6 Maintenance of Smart Health Data 185 10.3.7 Clinical Trial and Study 185 10.3.8 Crowdsourced Information Discovery 185 10.3.9 Enhanced Radiotherapy 186 10.3.10 Outbreak/Epidemic Prediction 186 10.4 Edge AI: For Smart Transformation of Healthcare 186 10.4.1 Role of Edge in Reshaping Healthcare 186 10.4.2 How AI Powers the Edge 187 10.5 Edge AI-Modernizing Human Machine Interface 188 10.5.1 Rural Medicine 188 10.5.2 Autonomous Monitoring of Hospital Rooms—A Case Study 188 10.6 Significance of Fuzzy in Healthcare 189 10.6.1 Fuzzy Logic—Outline 189 10.6.2 Fuzzy Logic-Based Smart Healthcare 190 10.6.3 Medical Diagnosis Using Fuzzy Logic for Decision Support Systems 191 10.6.4 Applications of Fuzzy Logic in Healthcare 193 10.7 Conclusion and Discussions 193 References 194 11 Video Conferencing (VC) Software Selection Using Fuzzy TOPSIS 197 Rekha Gupta 11.1 Introduction 197 11.2 Video Conferencing Software and Its Major Features 199 11.2.1 Video Conferencing/Meeting Software (VC/MS) for Higher Education Institutes 199 11.3 Fuzzy TOPSIS 203 11.3.1 Extension of TOPSIS Algorithm: Fuzzy TOPSIS 203 11.4 Sample Numerical Illustration 207 11.5 Conclusions 213 References 213 12 Estimation of Nonperforming Assets of Indian Commercial Banks Using Fuzzy AHP and Goal Programming 215 Kandarp Vidyasagar and Rajiv Kr. Dwivedi 12.1 Introduction 216 12.1.1 Basic Concepts of Fuzzy AHP and Goal Programming 217 12.2 Research Model 221 12.2.1 Average Growth Rate Calculation 227 12.3 Result and Discussion 233 12.4 Conclusion 234 References 234 13 Evaluation of Ergonomic Design for the Visual Display Terminal Operator at Static Work Under FMCDM Environment 237 Bipradas Bairagi 13.1 Introduction 238 13.2 Proposed Algorithm 240 13.3 An Illustrative Example on Ergonomic Design Evaluation 245 13.4 Conclusions 249 References 249 14 Optimization of Energy Generated from Ocean Wave Energy Using Fuzzy Logic 253 S. B. Goyal, Pradeep Bedi, Jugnesh Kumar and Prasenjit Chatterjee 14.1 Introduction 254 14.2 Control Approach in Wave Energy Systems 255 14.3 Related Work 257 14.4 Mathematical Modeling for Energy Conversion from Ocean Waves 259 14.5 Proposed Methodology 260 14.5.1 Wave Parameters 261 14.5.2 Fuzzy-Optimizer 262 14.6 Conclusion 264 References 264 15 The m-Polar Fuzzy TOPSIS Method for NTM Selection 267 Madan Jagtap and Prasad Karande 15.1 Introduction 268 15.2 Literature Review 268 15.3 Methodology 270 15.3.1 Steps of the mFS TOPSIS 270 15.4 Case Study 272 15.4.1 Effect of Analytical Hierarchy Process (AHP) Weight Calculation on the mFS TOPSIS Method 273 15.4.2 Effect of Shannon’s Entropy Weight Calculation on the m-Polar Fuzzy Set TOPSIS Method 277 15.5 Results and Discussions 281 15.5.1 Result Validation 281 15.6 Conclusions and Future Scope 283 References 284 16 Comparative Analysis on Material Handling Device Selection Using Hybrid FMCDM Methodology 287 Bipradas Bairagi 16.1 Introduction 288 16.2 MCDM Techniques 289 16.2.1 Fahp 289 16.2.2 Entropy Method as Weights (Influence) Evaluation Technique 290 16.3 The Proposed Hybrid and Super Hybrid FMCDM Approaches 291 16.3.1 Topsis 291 16.3.2 FMOORA Method 292 16.3.3 FVIKOR 292 16.3.4 Fuzzy Grey Theory (FGT) 293 16.3.5 COPRAS –G 293 16.3.6 Super Hybrid Algorithm 294 16.4 Illustrative Example 295 16.5 Results and Discussions 298 16.5.1 FTOPSIS 298 16.5.2 FMOORA 298 16.5.3 FVIKRA 298 16.5.4 Fuzzy Grey Theory (FGT) 299 16.5.5 COPRAS-G 299 16.5.6 Super Hybrid Approach (SHA) 299 16.6 Conclusions 302 References 302 17 Fuzzy MCDM on CCPM for Decision Making: A Case Study 305 Bimal K. Jena, Biswajit Das, Amarendra Baral and Sushanta Tripathy 17.1 Introduction 306 17.2 Literature Review 307 17.3 Objective of Research 308 17.4 Cluster Analysis 308 17.4.1 Hierarchical Clustering 309 17.4.2 Partitional Clustering 309 17.5 Clustering 310 17.6 Methodology 314 17.7 TOPSIS Method 316 17.8 Fuzzy TOPSIS Method 318 17.9 Conclusion 325 17.10 Scope of Future Study 326 References 326 Index 329

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Book SynopsisPrinciples of Soft Computing Using Python Programming An accessible guide to the revolutionary techniques of soft computing Soft 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. Principles of Soft Computing Using Python Programming provides readers with the knowledge required to apply soft computing models and techniques to real computational problems. Beginning with a foundational discussion of soft or fuzzy computing and its differences from hard computing, it describes different models for soft computing and

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Book SynopsisExamines 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.Trade Review"In this remarkable book, Dawson refines and develops synthetic 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." Larry Shapiro, University of Wisconsin "Minds and Machines 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." David A. Medler, The Medical College of Wisconsin "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 Understanding Cognitive Science. 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." Stefan C. Kremer, University of Guelph, Canada "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." David Spurrett, University of KwaZulu-Natal, Psychology in Society, 30, 2004, 77-79Table of ContentsList of Figures. List of Tables. 1. The Kids in the Hall. Synthetic Versus Analytic Traditions. . 2. Advantages and Disadvantages of Modeling. What Is A Model?. Advantages and Disadvantages of Models. . 3. Models of Data. An Example of a Model of Data. Properties of Models of Data. . 4. Mathematical Models. An Example Mathematical Model. Mathematical Models vs. Models of Data. . 5. Computer Simulations. A Sample Computer Simulation. Connectionist Models. Properties of Computer Simulations. . 6. First Steps Toward Synthetic Psychology. Introduction. Building a Thoughtless Walker. Step 1: Synthesis. Step 2: Emergence. Step 3: Analysis. Issues Concerning Synthetic Psychology. . 7. Uphill Analysis, Downhill Synthesis. Introduction. From Homeostats to Tortoises. Ashby’s Homeostat. Vehicles. Synthesis and Emergence: Some Modern Examples. The Law of Uphill Analysis and Downhill Synthesis. . 8. Connectionism As Synthetic Psychology. Introduction. Beyond Sensory Reflexes. Connectionism, Synthesis, and Representation. Summary and Conclusions. . 9. Building Associations. From Associationism To Connectionism. Building An Associative Memory. Beyond the Limitations of Hebb Learning. Associative Memory and Synthetic Psychology. . 10. Making Decisions. The Limits of Linearity. A Fundamental Nonlinearity. Building a Perceptron: A Nonlinear Associative Memory. The Psychology of Perceptrons. The Need for Layers. . 11. Sequences of Decisions. The Logic of Layers. Training Multilayered Networks. A Simple Case Study: Exclusive Or. A Second Case Study: Classifying Musical Chords. A Third Case Study: From Connectionism to Selectionism. . 12. From Synthesis To Analysis. Representing Musical Chords in a Pdp Network. Interpreting the Internal Structure of Value Unit Networks. Network Interpretation and Synthetic Psychology. . 13. From Here To Synthetic Psychology. References. Index

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Book SynopsisIn today’s data-driven world, more sophisticated algorithms for data processing are in high demand, mainly when the data cannot be handled with the help of traditional techniques. Self-learning and adaptive algorithms are now widely used by such leading giants that as Google, Tesla, Microsoft, and Facebook in their projects and applications. In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear. Including research relevant to those studying cybernetics, applied mathematics, statistics, engineering, and bioinformatics who are working in the areas of machine learning, artificial intelligence, complex system modeling and analysis, neural networks, and optimization, this is an ideal read for anyone interested in learning more about the fascinating new developments in machine learning.Trade ReviewThis guide explains how to apply methods using systems built by a combination of the neural network approach and fuzzy logic (neuro-fuzzy systems) to solve practical data classification problems in business. It describes methods aimed at handling the main types of uncertainties in data, using adaptive methods of fuzzy clustering; the use of Kohonen maps and their ensembles for fuzzy clustering tasks; and simulation results of these neuro-fuzzy architectures, their learning methods, self-organization, and clustering procedures. -- Annotation ©2019 * (protoview.com) *Table of ContentsIntroduction 1. Review of the Problem Area 2. Adaptive Methods of Fuzzy Clustering 3. Kohonen Maps and their Ensembles for Fuzzy Clustering Tasks 4. Simulation Results and Solutions for Practical Tasks Conclusion

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Book SynopsisNonlinear 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.The 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. Nonlinear Economic Models provides a sequel to Chaos and Nonlinear Models in Economics by the same editors.Trade Review'This collection provides valuable introductory material that is accessible to students and scholars interested in this research area.' -- Business HorizonsTable of ContentsContents: 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

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Table of Contents1. 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

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Book SynopsisSpin 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.Trade Review... 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 *Table of Contents1. 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

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book Synopsis

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Book SynopsisThis 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.

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Book SynopsisDeep 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.Trade Review'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'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&M University'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'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 AwardTable of Contents1. 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.

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Book SynopsisIn 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. Artificial Neural Network Applications in Business and Engineering 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.

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Book SynopsisLeverage Python source code to revolutionize your short selling strategy and to consistently make profits in bull, bear, and sideways marketsKey Features Understand techniques such as trend following, mean reversion, position sizing, and risk management in a short-selling context Implement Python source code to explore and develop your own investment strategy Test your trading strategies to limit risk and increase profits Book DescriptionIf 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. This 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. By 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.What you will learn Develop the mindset required to win the infinite, complex, random game called the stock market Demystify short selling in order to generate alpa in bull, bear, and sideways markets Generate ideas consistently on both sides of the portfolio Implement Python source code to engineer a statistically robust trading edge Develop superior risk management habits Build a long/short product that investors will find appealing Who this book is forThis 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.At 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.Table of ContentsTable of Contents The Stock Market Game 10 Classic Myths About Short-Selling Take a Walk on the Wild Short-Side Long/Short Methodologies: Absolute and Relative Regime Definition The Trading Edge is a Number, and Here is the Formula Improve Your Trading Edge Position Sizing: Money is Made in the Money Management Module Risk is a number Refining the Investment Universe The Long/Short Toolbox Signals and Execution Portfolio Management System Appendix

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Book SynopsisDesign 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.Table of ContentsTable of Contents Getting Started with Graph Learning Graph Theory for Graph Neural Networks Creating Node Representations with DeepWalk Improving Embeddings with Biased Random Walks in Node2Vec Including Node Features with Vanilla Neural Networks Introducing Graph Convolutional Networks Graph Attention Networks Scaling Graph Neural Networks with GraphSAGE Defining Expressiveness for Graph Classification Predicting Links with Graph Neural Networks Generating Graphs Using Graph Neural Networks Learning from Heterogeneous Graphs Temporal Graph Neural Networks Explaining Graph Neural Networks Forecasting Traffic Using A3T-GCN Detecting Anomalies Using Heterogeneous Graph Neural Networks Building a Recommender System Using LightGCN Unlocking the Potential of Graph Neural Networks for Real-Word Applications

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Book SynopsisGet 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.Table of ContentsTable of Contents What Is Generative AI? LangChain for LLM Apps Getting Started with LangChain Building Capable Assistants Building a Chatbot like ChatGPT Developing Software with Generative AI LLMs for Data Science Customizing LLMs and Their Output Generative AI in Production The Future of Generative Models

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