Machine learning Books

Book SynopsisEntering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.Table of ContentsPart I Python.- 1 About Python.- 2 What’s Python?.- 3 An Introductory Example.- 4 Basic Python.- 5 Intermediate Python.- 6 Advanced Python.- 7 Python for data analysis.- Part II Artificial Intelligence Basics.- 8 Introduction to artificial intelligence.- 9 Data wrangling.- 10 Regression.- 11 Classification.- 12 Clustering.- 13 Association Rules.- Part III Artificial Intelligence.- Implementations.- 14 Text Mining.- 15 Image Processing.- 16 Convolutional Neural Networks.- 17 Chatbot, Speech and NLP.- 18 Deep Convolutional Generative Adversarial Network.- 19 Neural style transfer.- 20 Reinforcement learning.- 21 References.

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Book SynopsisThis book focuses on research aspects of ensemble approaches of machine learning techniques that can be applied to address the big data problems. In this book, various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance are presented in several chapters. Each of them has separate functionalities, which can be leveraged to solve a specific set of big data applications. This book is a potential resource for various advances in the field of machine learning and data science to solve big data problems with many objectives. It has been observed from the literature that several works have been focused on the advancement of machine learning in various fields like biomedical, stock prediction, sentiment analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems. Table of ContentsDeep Learning for Supervised Learning.- Deep Learning for Unsupervised Learning.- Support Vector Machine for Regression.- Support Vector Machine for Classification.- Decision Tree for Regression.- Higher Order Neural Networks.- Competitive Learning.- Semi-supervised Learning.- Multi-objective Optimization Techniques.- Techniques for Feature Selection/Extraction.- Techniques for Task Relevant Big Data Analysis.- Techniques for Post Processing Task in Big Data Analysis.- Customer Relationship Management.

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Book SynopsisThis book provides a thorough overview of the evolution of privacy-preserving machine learning schemes over the last ten years, after discussing the importance of privacy-preserving techniques. In response to the diversity of Internet services, data services based on machine learning are now available for various applications, including risk assessment and image recognition. In light of open access to datasets and not fully trusted environments, machine learning-based applications face enormous security and privacy risks. In turn, it presents studies conducted to address privacy issues and a series of proposed solutions for ensuring privacy protection in machine learning tasks involving multiple parties. In closing, the book reviews state-of-the-art privacy-preserving techniques and examines the security threats they face.Table of ContentsIntroduction.- Secure Cooperative Learning in Early Years.- Outsourced Computation for Learning.- Secure Distributed Learning.- Learning with Differential Privacy.- Applications - Privacy-Preserving Image Processing.- Threats in Open Environment.- Conclusion.

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Book SynopsisEntering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.Table of ContentsPart I Python.- 1 About Python.- 2 What’s Python?.- 3 An Introductory Example.- 4 Basic Python.- 5 Intermediate Python.- 6 Advanced Python.- 7 Python for data analysis.- Part II Artificial Intelligence Basics.- 8 Introduction to artificial intelligence.- 9 Data wrangling.- 10 Regression.- 11 Classification.- 12 Clustering.- 13 Association Rules.- Part III Artificial Intelligence.- Implementations.- 14 Text Mining.- 15 Image Processing.- 16 Convolutional Neural Networks.- 17 Chatbot, Speech and NLP.- 18 Deep Convolutional Generative Adversarial Network.- 19 Neural style transfer.- 20 Reinforcement learning.- 21 References.

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Book SynopsisThis book is as an extension of previous book “Computer Vision and Machine Learning in Agriculture” for academicians, researchers, and professionals interested in solving the problems of agricultural plants and products for boosting production by rendering the advanced machine learning including deep learning tools and techniques to computer vision algorithms. The book contains 15 chapters. The first three chapters are devoted to crops harvesting, weed, and multi-class crops detection with the help of robots and UAVs through machine learning and deep learning algorithms for smart agriculture. Next, two chapters describe agricultural data retrievals and data collections. Chapters 6, 7, 8 and 9 focuses on yield estimation, crop maturity detection, agri-food product quality assessment, and medicinal plant recognition, respectively. The remaining six chapters concentrates on optimized disease recognition through computer vision-based machine and deep learning strategies.Table of ContentsHarvesting robots for smart agriculture.- Drone-based weed detection architectures using deep learning algorithms and real-time analytics.- A deep learning-based detection system of multi-class crops and orchards using a UAV.- Real-life agricultural data retrieval for large scale annotation flow optimization.- Design and analysis of IoT-based modern agriculture monitoring system for real time data collection.- Estimation of wheat yield based on precipitation and evapotranspiration using soft computing methods.- Coconut maturity recognition using convolutional neural network.- Agri food products quality assessment methods.- Medicinal plant recognition from leaf images using deep learning.- ESMO based plant leaf disease identification: A machine learning approach.- Deep learning-based cuali flower disease classification.- An Intelligent System for Crop Disease Identification and Dispersion Forecasting in SriLanka.- Apple leaves diseases detection using deep convolutional neural networks and transfer learning.- A deep learning paradigm for detection and segmentation of plant leaves diseases.- Early-stage prediction of plant leaf diseases using deep learning models.

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Book SynopsisDeep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.Table of ContentsContents1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 What is Deep Reinforcement Learning? . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Three Machine Learning Paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.3 Overview of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 Tabular Value-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.1 Sequential Decision Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.2 Tabular Value-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.3 Classic Gym Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 Approximating the Value Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633.1 Large, High-Dimensional, Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663.2 Deep Value-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693.3 Atari 2600 Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 833.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 863.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 874 Policy-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 894.1 Continuous Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.2 Policy-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 944.3 Locomotion and Visuo-Motor Environments . . . . . . . . . . . . . . . . . . . . 1114.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1154.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165 Model-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1195.1 Dynamics Models of High-Dimensional Problems . . . . . . . . . . . . . . . 1225.2 Learning and Planning Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1235.3 High-dimensional Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1365.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142viiviii CONTENTS5.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1446 Two-Agent Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1476.1 Two-Agent Zero-Sum Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1506.2 Tabula Rasa Self-Play Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1566.3 Self-Play Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1786.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1866.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1887 Multi-Agent Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1917.1 Multi-Agent Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1937.2 Multi-Agent Reinforcement Learning Agents . . . . . . . . . . . . . . . . . . . . 2027.3 Multi-Agent Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2147.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2217.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2238 Hierarchical Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 2258.1 Granularity of the Structure of Problems . . . . . . . . . . . . . . . . . . . . . . . 2278.2 Divide and Conquer for Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2298.3 Hierarchical Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2358.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2408.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2419 Meta Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2439.1 Learning to Learn Related Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2469.2 Transfer Learning and Meta Learning Agents . . . . . . . . . . . . . . . . . . . 2479.3 Meta-Learning Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2619.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2679.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26810 Further Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27110.1 Developments in Deep Reinforcement Learning . . . . . . . . . . . . . . . . . 27110.2 Main Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27410.3 The Future of Articial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279A Deep Reinforcement Learning Suites . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283A.1 Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284A.2 Agent Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285A.3 Deep Learning Suites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286B Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287B.1 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287B.2 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294B.3 Datasets and Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311CONTENTS ixC Mathematical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323C.1 Sets and Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323C.2 Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326C.3 Derivative of an Expectation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334C.4 Bellman Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381x CONTENTSContents1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 What is Deep Reinforcement Learning? . . . . . . . . . . . . . . . . . . . . . . . . 11.1.1 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.1.2 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.1.3 Deep Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 31.1.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.1.5 Four Related Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.1.5.1 Psychology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.1.5.2 Mathematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.1.5.3 Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101.1.5.4 Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.2 Three Machine Learning Paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.2.1 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141.2.2 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151.2.3 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161.3 Overview of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171.3.1 Prerequisite Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181.3.2 Structure of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 Tabular Value-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.1 Sequential Decision Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.2 Tabular Value-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.2.1 Agent and Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.2.2 Markov Decision Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282.2.2.1 State ( . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292.2.2.2 Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302.2.2.3 Transition )0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312.2.2.4 Reward '0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332.2.2.5 Discount Factor W . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332.2.2.6 Policy Function c . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332.2.3 MDP Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34xixii Contents2.2.3.1 Trace g . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352.2.3.2 State Value + . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362.2.3.3 State-Action Value & . . . . . . . . . . . . . . . . . . . . . . . . . . 372.2.3.4 Reinforcement Learning Objective . . . . . . . . . . . . . . 382.2.3.5 Bellman Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392.2.4 MDP Solution Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392.2.4.1 Hands On: Value Iteration in Gym . . . . . . . . . . . . . . . 412.2.4.2 Model-Free Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 442.2.4.3 Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 472.2.4.4 O-Policy Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 492.2.4.5 Hands On: Q-learning on Taxi . . . . . . . . . . . . . . . . . . 522.3 Classic Gym Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552.3.1 Mountain Car and Cartpole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552.3.2 Path Planning and Board Games . . . . . . . . . . . . . . . . . . . . . . . . 562.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 603 Approximating the Value Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633.1 Large, High-Dimensional, Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663.1.1 Atari Arcade Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673.1.2 Real-Time Strategy and Video Games . . . . . . . . . . . . . . . . . . . . 683.2 Deep Value-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693.2.1 Generalization of Large Problem with Deep Learning . . . . . 693.2.1.1 Minimizing Supervised Target Loss . . . . . . . . . . . . . 693.2.1.2 Bootstrapping Q-Values . . . . . . . . . . . . . . . . . . . . . . . 703.2.1.3 Deep Reinforcement Learning Target-Error . . . . . 713.2.2 Three Problems: Coverage, Correlation, Convergence . . . . . 723.2.2.1 Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723.2.2.2 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723.2.2.3 Convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733.2.3 Stable Deep Value-Based Learning . . . . . . . . . . . . . . . . . . . . . . 743.2.3.1 Decorrelating States . . . . . . . . . . . . . . . . . . . . . . . . . . . 753.2.3.2 Infrequent Updates of Target Weights . . . . . . . . . . . 763.2.3.3 Hands On: DQN and Breakout Gym Example . . . . . 763.2.4 Improving Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803.2.4.1 Overestimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 813.2.4.2 Distributional Methods . . . . . . . . . . . . . . . . . . . . . . . . 833.3 Atari 2600 Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 833.3.1 Network Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 843.3.2 Benchmarking Atari . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 853.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 863.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87Contents xiii4 Policy-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 894.1 Continuous Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.1.1 Continuous Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.1.2 Stochastic Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 914.1.3 Environments: Gym and MuJoCo . . . . . . . . . . . . . . . . . . . . . . . 924.1.3.1 Robotics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 924.1.3.2 Physics Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 924.1.3.3 Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 944.2 Policy-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 944.2.1 Policy-Based Algorithm: REINFORCE . . . . . . . . . . . . . . . . . . . 954.2.2 Bias-Variance trade-o in Policy-Based Methods . . . . . . . . . 984.2.3 Actor Critic Bootstrapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 994.2.4 Baseline Subtraction with Advantage Function . . . . . . . . . . . 1014.2.5 Trust Region Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1044.2.6 Entropy and Exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1064.2.7 Deterministic Policy Gradient . . . . . . . . . . . . . . . . . . . . . . . . . . 1074.2.8 Hands On: PPO and DDPG MuJoCo Examples . . . . . . . . . . . . . 1104.3 Locomotion and Visuo-Motor Environments . . . . . . . . . . . . . . . . . . . . 1114.3.1 Locomotion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1114.3.2 Visuo-Motor Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1134.3.3 Benchmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1154.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1165 Model-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1195.1 Dynamics Models of High-Dimensional Problems . . . . . . . . . . . . . . . 1225.2 Learning and Planning Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1235.2.1 Learning the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1285.2.1.1 Modeling Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . 1285.2.1.2 Latent Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1295.2.2 Planning with the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1315.2.2.1 Trajectory Rollouts and Model-Predictive Control 1325.2.2.2 End-to-end Learning and Planning-by-Network . 1335.3 High-dimensional Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1365.3.1 Overview of Model-Based Experiments . . . . . . . . . . . . . . . . . . 1375.3.2 Small Navigation Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1385.3.3 Robotic Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1395.3.4 Games Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1395.3.5 Hands On: PlaNet Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1415.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1425.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144xiv Contents6 Two-Agent Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1476.1 Two-Agent Zero-Sum Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1506.1.1 The Diculty of Playing Go . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1526.1.2 AlphaGo Achievements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1556.2 Tabula Rasa Self-Play Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1566.2.1 Move-Level Self Play . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1606.2.1.1 Minimax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1616.2.1.2 Monte Carlo Tree Search . . . . . . . . . . . . . . . . . . . . . . 1646.2.2 Example-Level Self Play . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1716.2.2.1 Policy and Value Network . . . . . . . . . . . . . . . . . . . . . 1726.2.2.2 Stability and Exploration . . . . . . . . . . . . . . . . . . . . . . 1726.2.3 Tournament-Level Self Play . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1746.2.3.1 Self-Play Curriculum Learning . . . . . . . . . . . . . . . . . 1756.2.3.2 Supervised Curriculum Learning . . . . . . . . . . . . . . . 1756.3 Self-Play Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1786.3.1 How to Design a World Class Go Program? . . . . . . . . . . . . . . 1786.3.2 AlphaGo Zero Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1806.3.3 AlphaZero . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1816.3.4 Open Self-Play Frameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1836.3.5 Hands On: Hex in Polygames Example . . . . . . . . . . . . . . . . . . . . 1846.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1866.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1887 Multi-Agent Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1917.1 Multi-Agent Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1937.1.1 Competitive Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1967.1.2 Cooperative Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1977.1.3 Mixed Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1987.1.4 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2007.1.4.1 Partial Observability . . . . . . . . . . . . . . . . . . . . . . . . . . 2017.1.4.2 Nonstationary Environments . . . . . . . . . . . . . . . . . . 2017.1.4.3 Large State Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2027.2 Multi-Agent Reinforcement Learning Agents . . . . . . . . . . . . . . . . . . . . 2027.2.1 Competitive Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2037.2.1.1 Counterfactual Regret Minimization . . . . . . . . . . . . 2037.2.1.2 Deep Counterfactual Regret Minimization . . . . . . . 2047.2.2 Cooperative Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2067.2.2.1 Centralized Training/Decentralized Execution . . . 2067.2.2.2 Opponent Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . 2077.2.2.3 Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2087.2.2.4 Psychology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2087.2.3 Mixed Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2097.2.3.1 Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . 2097.2.3.2 Swarm Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2117.2.3.3 Population-Based Training . . . . . . . . . . . . . . . . . . . . . 212Contents xv7.2.3.4 Self-Play Leagues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2137.3 Multi-Agent Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2147.3.1 Competitive Behavior: Poker . . . . . . . . . . . . . . . . . . . . . . . . . . . 2147.3.2 Cooperative Behavior: Hide and Seek. . . . . . . . . . . . . . . . . . . . 2167.3.3 Mixed Behavior: Capture the Flag and StarCraft . . . . . . . . . . 2187.3.4 Hands On: Hide and Seek in the Gym Example . . . . . . . . . . . . 2207.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2217.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2238 Hierarchical Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 2258.1 Granularity of the Structure of Problems . . . . . . . . . . . . . . . . . . . . . . . 2278.1.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2278.1.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2288.2 Divide and Conquer for Agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2298.2.1 The Options Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2298.2.2 Finding Subgoals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2318.2.3 Overview of Hierarchical Algorithms . . . . . . . . . . . . . . . . . . . . 2318.2.3.1 Tabular . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2328.2.3.2 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2328.3 Hierarchical Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2358.3.1 Four Rooms and Robot Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . 2358.3.2 Montezuma’s Revenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2368.3.3 Multi-Agent Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2388.3.4 Hands On: Hierarchical Actor Citic Example . . . . . . . . . . . . . . 2388.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2408.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2419 Meta Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2439.1 Learning to Learn Related Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2469.2 Transfer Learning and Meta Learning Agents . . . . . . . . . . . . . . . . . . . 2479.2.1 Transfer Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2489.2.1.1 Task Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2489.2.1.2 Pretraining and Finetuning . . . . . . . . . . . . . . . . . . . . 2499.2.1.3 Hands-on: Pretraining Example . . . . . . . . . . . . . . . . . 2499.2.1.4 Multi-task learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2509.2.1.5 Domain Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . 2519.2.2 Meta Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2539.2.2.1 Evaluating Few-Shot Learning Problems . . . . . . . . 2539.2.2.2 Deep Meta Learning Algorithms . . . . . . . . . . . . . . . 2549.2.2.3 Recurrent Meta Learning . . . . . . . . . . . . . . . . . . . . . . 2569.2.2.4 Model-Agnostic Meta Learning . . . . . . . . . . . . . . . . . 2579.2.2.5 Hyperparameter Optimization . . . . . . . . . . . . . . . . . 2599.2.2.6 Meta Learning and Curriculum Learning . . . . . . . . 2609.2.2.7 From Few-Shot to Zero-Shot Learning . . . . . . . . . . 2609.3 Meta-Learning Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261xvi Contents9.3.1 Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2629.3.2 Natural Language Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 2639.3.3 Meta Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2639.3.4 Meta World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2649.3.5 Alchemy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2659.3.6 Hands-on: Meta World Example . . . . . . . . . . . . . . . . . . . . . . . . . 2669.4 Summary and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2679.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26810 Further Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27110.1 Developments in Deep Reinforcement Learning . . . . . . . . . . . . . . . . . 27110.1.1 Tabular and Single-Agent Methods . . . . . . . . . . . . . . . . . . . . . . 27210.1.2 Deep Learning Model-Free Methods . . . . . . . . . . . . . . . . . . . . . 27210.1.3 Multi-Agent and Imperfect Information . . . . . . . . . . . . . . . . . . 27210.1.4 A Framework for Learning by Doing . . . . . . . . . . . . . . . . . . . . 27310.2 Main Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27410.2.1 Latent Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27510.2.2 Self Play . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27510.2.3 Hierarchical Reinforcement Learning . . . . . . . . . . . . . . . . . . . . 27510.2.4 Transfer Learning and Meta Learning . . . . . . . . . . . . . . . . . . . 27610.2.5 Population-Based Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27610.2.6 Exploration and Intrinsic Motivation . . . . . . . . . . . . . . . . . . . . 27710.2.7 Explainable AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27810.2.8 Generalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27810.3 The Future of Articial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279A Deep Reinforcement Learning Suites . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283A.1 Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284A.2 Agent Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285A.3 Deep Learning Suites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286B Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287B.1 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287B.1.1 Training Set and Test Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288B.1.2 Curse of Dimensionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289B.1.3 Overtting and the Bias-Variance Trade-O . . . . . . . . . . . . . . 290B.2 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294B.2.1 Weights, Neurons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294B.2.2 Backpropagation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295B.2.3 End-to-end Feature Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 297B.2.4 Convolutional Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300B.2.5 Recurrent Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303B.2.6 More Network Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . 305B.2.7 Overtting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310B.3 Datasets and Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311Contents xviiB.3.1 Keras, TensorFlow, PyTorch . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312B.3.2 MNIST and ImageNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313B.3.3 GPU Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315B.3.4 Hands On: Classication Example . . . . . . . . . . . . . . . . . . . . . . . . 316B.3.5 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319C Mathematical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323C.1 Sets and Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323C.1.1 Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323C.1.2 Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325C.2 Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326C.2.1 Discrete Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . 326C.2.2 Continuous Probability Distributions . . . . . . . . . . . . . . . . . . . . 327C.2.3 Conditional Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329C.2.4 Expectation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330C.2.4.1 Expectation of a Random Variable . . . . . . . . . . . . . . 330C.2.4.2 Expectation of a Function of a Random Variable . 331C.2.5 Information Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332C.2.5.1 Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332C.2.5.2 Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332C.2.5.3 Cross-entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333C.2.5.4 Kullback-Leibler Divergence . . . . . . . . . . . . . . . . . . . 333C.3 Derivative of an Expectation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334C.4 Bellman Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381

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Book SynopsisThis book provides both the developers and the users with an awareness of the challenges and opportunities of advancements in healthcare paradigm with the application and availability of advanced hardware, software, tools, technique or algorithm development stemming the Internet of Things. The book helps readers to bridge the gap in their three understanding of three major domains and their interconnections: Hardware tested and software APP development for data collection, intelligent protocols for analysis and knowledge extraction. Medical expertise to interpret extracted knowledge towards disease prediction or diagnosis and support. Security experts to ensure data correctness for precise advice. The book provides state-of-the-art overviews by active researchers, technically elaborating healthcare architectures/frameworks, protocols, algorithms, methodologies followed by experimental results and evaluation. Future direction and scope will be precisely documented for interested readers.Table of ContentsPart 1 IoT based Smart Healthcare.- Chapter 1 Introduction.- Chapter 2 Architecture for Smart Healthcare: Cloud vs Edge.- Chapter 3 Main Challenges and Concerns of Health IoT Data.- Part 2 Context and Body Vitals Monitoring Systems.- Chapter 4 Human Activity Recognition Systems Based on Sensor Data using Machine Learning.- Chapter 5 Human Activity Recognition Systems Based on Audio-Video Data using Machine Learning and Deep learning.- Chapter 6 Review of Body Vitals Monitoring Systems for Disease Prediction.- Chapter 7 Review of Context Aware System Implementations.- Part 3 Social Sensing Applications for Public Health.- Chapter 8 Types of Social Sensing Data.- Chapter 9 Social Data Analysis Techniques and Applications.- Chapter 10 Challenges and Limitations of Social Data Analysis Approaches.- Part 4 Reliability, Security and Privacy of Health Data.- Chapter 11 Quality of Service vs Quality of Experience for Real-time Smart Healthcare.- Chapter 12 Security and Privacy Issues of Health Data.- Chapter 13 Review of Performance Metrics and Corrective Measures for Health Data Analysis.

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Book SynopsisThis book aims to sort out the clear logic of the development of machine learning-driven privacy preservation in IoTs, including the advantages and disadvantages, as well as the future directions in this under-explored domain. In big data era, an increasingly massive volume of data is generated and transmitted in Internet of Things (IoTs), which poses great threats to privacy protection. Motivated by this, an emerging research topic, machine learning-driven privacy preservation, is fast booming to address various and diverse demands of IoTs. However, there is no existing literature discussion on this topic in a systematically manner. The issues of existing privacy protection methods (differential privacy, clustering, anonymity, etc.) for IoTs, such as low data utility, high communication overload, and unbalanced trade-off, are identified to the necessity of machine learning-driven privacy preservation. Besides, the leading and emerging attacks pose further threats to privacy protection in this scenario. To mitigate the negative impact, machine learning-driven privacy preservation methods for IoTs are discussed in detail on both the advantages and flaws, which is followed by potentially promising research directions. Readers may trace timely contributions on machine learning-driven privacy preservation in IoTs. The advances cover different applications, such as cyber-physical systems, fog computing, and location-based services. This book will be of interest to forthcoming scientists, policymakers, researchers, and postgraduates. Table of Contents· Chapter 1: Introduction o Privacy research landscape o Machine learning driven privacy preservation overview o Contribution of this monograph o Outline of the monograph · Chapter 2: Current Methods of Privacy Protection in IoTs o Cryptography based methods o Differential privacy methods o Anonymity-based methods o Clustering-based methods · Chapter 3: Decentralized Privacy Protection of IoTs using Blockchain-Enabled Federated Learning o Overview o System Modelling o Decentralized Privacy Protocols o Blockchain-enabled Federated Learning · Chapter 4: Personalized Privacy Protection of IoTs using GAN-Enhanced Differential Privacy o Overview o System Modelling o Personalized Privacy o GAN-Enhanced Differential Privacy · Chapter 5: Hybrid Privacy Protection of IoT using Reinforcement Learning o Overview o System Modelling o Hybrid Privacy o Markov Decision Process and Reinforcement Learning · Chapter 6: Future Directions o Trade-off optimization o Privacy preservation of digital twin o Privacy-preserving federated learning o Federated generative adversarial nets · Chapter 7: Summary and Outlook

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Book SynopsisThis interdisciplinary book incorporates various aspects of environment, ecology, and natural disaster management including cognitive informatics and computing. It fosters research innovation and discovery on basic science and information technology for addressing various environmental problems, while providing the right solutions in environment, ecology, and disaster management. This book is a unique resource for researchers and practitioners of energy informatics in various scientific, technological, engineering, and social fields to disseminate original research on the application of digital technology and information management theory and practice to facilitate the global transition toward sustainable and resilient energy systems. Cognitive informatics is also the need of the hour and deals with cutting-edge and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, computation, software engineering, AI, cybernetics, cognitive science, neuropsychology, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences, which this book also presents.Table of ContentsChapter 1: Perspectives of Environmental Informatics and Systems Analysis Chapter 2: Planning of energy, environmental and ecological management systems Chapter 3: Simulation, optimization and Environmental decision support Chapter 4: Environmental geomatics - GIS, RS and other spatial information technologies Chapter 5: Cloud Computing, Big Data, Machine Learning in Environmental & Disaster Management Chapter 6: Urbanization – Smart City Supported with Environmental and Energy Systems & Technologies Chapter 7: Climate Change, Environmental Monitoring using Emerging Technologies, AI & Robotics Chapter 8: Modeling, Computer Graphics and Big Data in Energy Management Chapter 9: Monitoring and control of Smart Buildings, including Smart Grid interoperability Chapter 10: Basics and Emergence of Environmental and Energy Informatics Chapter 11: Emerging and Intelligent Systems in Environment and Ecology Chapter 12: Building product disclosure and optimization: Effect on Ecological system Chapter 13: AI in waste management: The savage of Environment ​

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Book SynopsisThis textbook explains the fundamentals of Markov Chain Monte Carlo (MCMC) without assuming advanced knowledge of mathematics and programming. MCMC is a powerful technique that can be used to integrate complicated functions or to handle complicated probability distributions. MCMC is frequently used in diverse fields where statistical methods are important – e.g. Bayesian statistics, quantum physics, machine learning, computer science, computational biology, and mathematical economics. This book aims to equip readers with a sound understanding of MCMC and enable them to write simulation codes by themselves. The content consists of six chapters. Following Chap. 2, which introduces readers to the Monte Carlo algorithm and highlights the advantages of MCMC, Chap. 3 presents the general aspects of MCMC. Chap. 4 illustrates the essence of MCMC through the simple example of the Metropolis algorithm. In turn, Chap. 5 explains the HMC algorithm, Gibbs sampling algorithm and Metropolis-Hastings algorithm, discussing their pros, cons and pitfalls. Lastly, Chap. 6 presents several applications of MCMC. Including a wealth of examples and exercises with solutions, as well as sample codes and further math topics in the Appendix, this book offers a valuable asset for students and beginners in various fields. Table of ContentsChapter 1: Introduction.- Chapter 2: What is the Monte Carlo method?.- Chapter 3: General Aspects of Markov Chain Monte Carlo.- Chapter 4: Metropolis Algorithm.- Chapter 5: Other Useful Algorithms.- Chapter 6: Applications of Markov Chain Monte Carlo.

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Book SynopsisThe volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.Trade Review“Each application is described with sufficient detail to give the reader an understanding of how AI is used and how its use compares with older tools used for the same purposes. The writing is clear … and an excessive use of acronyms. Relevant images and tables enhance the reader’s understanding; many references accompany each chapter. This book should appeal to those interested in either AI or the field of solar astronomy.” (G. R. Mayforth, Computing Reviews, November 22, 2023)Table of ContentsChapter 1: Introduction Chapter 2: Classical deep learning models Chapter 3: Deep learning in solar image classification tasks Chapter 4: Deep learning in solar object detection tasks · Active Region (AR) detection · EUV waves detection Chapter 5: Deep learning in solar image generation tasks · Deconvolution of aperture synthesis · Recovering over-exposed solar image · Generating magnetogram from EUV image · Generating magnetogram from H-alpha Chapter 6: Deep learning in solar forecasting tasks · Flare forecast · F10.7c forecast

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Book SynopsisThis book features research papers presented at the 5th International Conference on Intelligent Sustainable Systems (ICISS 2022), held at SCAD College of Engineering and Technology, Tirunelveli, Tamil Nadu, India, during February 17–18, 2022. The book discusses latest research works that discusses the tools, methodologies, practices, and applications of sustainable systems and computational intelligence methodologies. The book is beneficial for readers from both academia and industry.Table of ContentsChapter 1. Lung Ultrasound Covid-19 Detection using Deep Feature Recursive Neural Network.- Chapter 2. Predicting New York Taxi Trip Duration based on Regression Analysis using ML and Time Series Forecasting using DL.- Chapter 3. Implementation of Classical Error Control Codes for Memory Storage Systems using VERILOG.- Chapter 4. Parkinson’s Disease Detection using Machine Learning.- Chapter 5. Sustainable Consumption: An Approach to Achieve the Sustainable Environment in India.- Chapter 6. The Concept of a Digital Marketing Communication Model for Higher Education Institutions.- Chapter 7. A Lightweight Image Colorization Model based on U-Net Architecture.- Chapter 8. Comparative Analysis of Obesity Level Estimation based on Life-Style using Machine Learning.- Chapter 9. An empirical Study on Millennials' Adoption of Mobile Wallets.- Chapter 10. AIoT Based Smart Mirror.- Chapter 11. AI Assisted College Recommendation System.- Chapter 12. An Agent-based Model to Predict the Emergence of Student Protests in Public Higher Education Institution.- Chapter 13. High Accuracy for Hyperspectral Image Classification using Hybrid Spectral 3-D-2-D CNN.- Chapter 14. Design Smart Curtain Using Light Dependent Resistor.- Chapter 15. Machine Learning Assisted Binary and Multiclass Parkinson's Disease Detection. etc.

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Book SynopsisThe book contains select proceedings of the 3rd International Conference on Data, Engineering, and Applications (IDEA 2021). It includes papers from experts in industry and academia that address state-of-the-art research in the areas of big data, data mining, machine learning, data science, and their associated learning systems and applications. This book will be a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of big data applications.Table of Contents1. Medical Assistance Chatbot using Deep Learning.- 2. Distortion Controlled Secure Reversible Data Hiding in H.264 videos.- 3. A Method for improving Efficiency and Security of FANET using Chaotic Black Hole Optimization based Routing (BHOR) Technique.- 4. Machine Learning Techniques for Intrusion Detection System: A Survey.- 5. Software Fault Detection by using Rider Optimization Algorithm (ROA) based Deep Neural Network (DNN).- 6. An Approach for Predicting Admissions in Post Graduate Program by using Machine Learning.- 7. A Survey on Various Representation Learning of Hypergraph for Unsupervised Feature Selection.- 8. A brief study of time series forecasting technique using linear regression, SVM, LSTM, ARIMA and SARIMA.- 9. Adoption of Blockchain Technology for Storage & Verification of Educational Documents.- 10. Obstacle Collision Prediction model for Path Planning Using Obstacle Trajectory Clustering.

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Book SynopsisThis book presents a collection of high-quality, peer-reviewed research papers from the 7th International Conference on Information System Design and Intelligent Applications (India 2022), held at BVRIT Hyderabad College of Engineering for Women, Hyderabad, Telangana, India, from February 25 to 26, 2022. It covers a wide range of topics in computer science and information technology, including data mining and data warehousing, high-performance computing, parallel and distributed computing, computational intelligence, soft computing, big data, cloud computing, grid computing and cognitive computing.

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Book SynopsisThis book constitutes the refereed proceedings of the Third International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021. The papers are organized according to the following topical sections: data science and big data; image processing and computer vision; machine learning and computational intelligence; network and cybersecurity. This book aims to develop an understanding of image processing, networks, and data modeling by using various machine learning algorithms for a wide range of real-world applications. In addition to providing basic principles of data processing, this book teaches standard models and algorithms for data and image analysis. Table of ContentsA Methodological review of Time Series Forecasting with Deep Learning Model : A Case study on Electricity Load and Price Prediction.- A Robust Secure Access Entrance Method Based on Multi Model Biometric Credentials Iris and Finger Print.- Prostate Cancer Grading using Multistage DeepNeural Networks.

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Book SynopsisThe book is a collection of best selected research papers presented at the International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications (ICMISC 2022) held during 28 – 29 March 2022 at CMR Institute of Technology, Hyderabad, Telangana, India. This book will contain the articles on current trends of machine learning, internet of things, and smart cities applications emphasizing on multi-disciplinary research in the area of artificial intelligence and cyber physical systems. The book is a great resource for scientists, research scholars and PG students to formulate their research ideas and find the future directions in these areas. Further, this book serves as a reference work to understand the latest technologies by practice engineers across the globe.Table of ContentsPrognostic Investigation into Melancholic Maladies in Hinterlands.- Security Threats in Healthcare Systems.- A Review on Behavioral Biometric GAIT Recognition.- Vital Role of 2D CNN in Brain Malignancy.- Design and Development of IoT based Intelligent Cattle Shed Management.- Review Paper on Technologies to curb Noise Pollution in No Honking Zones.- Radial Basis Neural Network Trained Minimum Snap Trajectory for Quadrotor.- Detection of Fraudulent Credit Card Transactions in Real-Time using SparkML and Kafka.- Robust and Scalable Network Monitoring System using Apache Spark.- Managing data protection and privacy on cloud.- Human Posture Monitoring.- Automation in Project Management 4.0 with Artificial Intelligence.- A state of art Approach to Question Generation Techniques.- Intelligent Information System for Detection of Covid-19 based on AI.- Plant Quality Assessment and Disease Identification System using AI.- Image-based Plant Disease Detection and classification using Deep Convolution Neural Network.

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Book SynopsisThis book constitutes refereed proceedings of the 4th International Conference on Recent Trends in Advanced Computing - Computer Vision and Machine Intelligence Paradigms for Sustainable Development Goals. This book covers novel and state-of-the-art methods in computer vision coupled with intelligent techniques including machine learning, deep learning, and soft computing techniques. The contents of this book will be useful to researchers from industry and academia. This book includes contemporary innovations, trends, and concerns in computer vision with recommended solutions to real-world problems adhering to sustainable development from researchers across industry and academia. This book serves as a valuable reference resource for academics and researchers across the globe.Table of ContentsPTZ-camera-based facial expression analysis using faster R-CNN for student engagement recognitionConvergence Perceptual Model for Computing Time-Series-Data on Fog-EnvironmentLocalized Super Resolution for Foreground Images using U-Net and MR-CNNSMS Spam Classification Using PSO-C4.5Automated Sorting, Grading of Fruits Based on Internal and External Quality Assessment Using HSI, Deep CNNPest Detection using Improvised YOLO ArchitectureClassification of Fungi Effected Psidium Guajava Leaves using ML and DL TechniquesDeep Learning Based Recognition of Plant DiseasesArtificial Cognition of Temporal Events using Recurrent Point Process NetworksOn the Performance of Energy Efficient Video Transmission over LEACH based protocol in WSNHybridization of Texture Features for Identification of Bi-lingual Scripts from Camera Images at WordlevelAdvanced Algorithmic Techniques for Topic Prediction and Recommendation - An AnalysisImplementation of an automatic EEG feature extraction with Gated Recurrent Neural Network for Emotion Recognition.

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Book SynopsisThis book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike.Table of ContentsChapter 1. Distributed Nesterov-Like Accelerated Algorithms in Networked Systems with Directed Communications.- Chapter 2. Distributed Stochastic Projected Gradient Algorithms for Composite Constrained Optimization in Networked Systems.- Chapter 3. Distributed Proximal Stochastic Gradient Algorithms for Coupled Composite Optimization in Networked Systems.- Chapter 4. Distributed Subgradient Algorithms Based on Event-Triggered Strategy in Networked Systems.- Chapter 5. Distributed Accelerated Stochastic Algorithms Based on Event-Triggered Strategy in Networked Systems.- Chapter 6. Event-Triggered Based Distributed Optimal Economic Dispatch in Smart Grids.- Chapter 7. Fast Distributed Optimal Economic Dispatch in Dynamic Smart Grids with Directed Communications.- Chapter 8. Accelerated Distributed Optimal Economic Dispatch in Smart Grids with Directed Communications.- Chapter 9. Privacy Preserving Distributed Online Learning with Time-Varying and Directed Communications.

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Book SynopsisChapter 1: Artificial Intelligence in Precision Medicine and Digital Health.- Chapter 2: Revisiting Doob's Theorem on Posterior Consistency.- Chapter 3: The Central Limit Theorem in High-dimension.- Chapter 4: An Introduction to Deep Learning.- Chapter 5: The R Language and its Use in Statistics.- Chapter 6: Large Deviation Asymptotics for Systems with Fractional Noise.- Chapter 7: High dimensional Wigner matrices with general independent entries.- Chapter 8: Data Analysis after Record Linkage: Sources of Error, Consequences, and Possible Solutions.- Chapter 9: Statistical Inference of Network Data: Past, Present, and Future.- Chapter 10: Current topics in group testing.

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Book SynopsisThis open access book discusses the theory and methods of hypergraph computation. Many underlying relationships among data can be represented using graphs, for example in the areas including computer vision, molecular chemistry, molecular biology, etc. In the last decade, methods like graph-based learning and neural network methods have been developed to process such data, they are particularly suitable for handling relational learning tasks. In many real-world problems, however, relationships among the objects of our interest are more complex than pair-wise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for learning tasks. Hypergraph, as a generation of graph, has shown superior performance on modelling complex correlations compared with graph. Recent years have witnessed a great popularity of researches on hypergraph-related AI methods, which have been used in computer vision, social media analysis, etc. We summarize these attempts as a new computing paradigm, called hypergraph computation, which is to formulate the high-order correlations underneath the data using hypergraph, and then conduct semantic computing on the hypergraph for different applications. The content of this book consists of hypergraph computation paradigms, hypergraph modelling, hypergraph structure evolution, hypergraph neural networks, and applications of hypergraph computation in different fields. We further summarize recent achievements and future directions on hypergraph computation in this book.Table of Contents

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Book SynopsisText-to-speech (TTS) aims to synthesize intelligible and natural speech based on the given text. It is a hot topic in language, speech, and machine learning research and has broad applications in industry. This book introduces neural network-based TTS in the era of deep learning, aiming to provide a good understanding of neural TTS, current research and applications, and the future research trend. This book first introduces the history of TTS technologies and overviews neural TTS, and provides preliminary knowledge on language and speech processing, neural networks and deep learning, and deep generative models. It then introduces neural TTS from the perspective of key components (text analyses, acoustic models, vocoders, and end-to-end models) and advanced topics (expressive and controllable, robust, model-efficient, and data-efficient TTS). It also points some future research directions and collects some resources related to TTS. This book is the first to introduce neural TTS in a comprehensive and easy-to-understand way and can serve both academic researchers and industry practitioners working on TTS.Table of Contents

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Book SynopsisMaster the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. The book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in Python and Stan. Whether you’re a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory.The key features of this indispensable book include: A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise. 100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension. A comprehensive guide to Sumio Watanabe’s groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians. Detailed source programs and Stan codes that will enhance readers’ grasp of the mathematical concepts presented. A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting. Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!Table of ContentsOver view of Watanabe's Bayes.- Introduction to Watanabe Bayesian Theory.- MCMC and Stan.- Mathematical Preparation.- Regular Statistical Models.- Information Criteria.- Algebraic Geometry.- The Essence of WAOIC.- WBIC and Its Application to Machine Learning.

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Book SynopsisThis book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning. Table of ContentsChapter 1 Introduction to Machine learning and Supervised Learning.- Chapter 2 Perceptron.- Chapter 3 K-Nearest-Neighbor.- Chapter 4 The Naïve Bayes Method.- Chapter 5 Decision Tree.- Chapter 6 Logistic Regression and Maximum Entropy Model.- Chapter 7 Support Vector Machine.- Chapter 8 Boosting.- Chapter 9 EM Algorithm and Its Extensions.- Chapter 10 Hidden Markov Model.- Chapter 11 Conditional Random Field.

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Book SynopsisThis book introduces and provides an analysis of the basic concepts of algorithms, data, and computation and discusses the role of algorithms in ruling and shaping our world. It provides a clear understanding of the power and impact on humanity of the pervasive use of algorithms.From Algorithms to Thinking Machines combines a layman's approach with a well-founded scientific description to discuss both principles and applications of algorithms, Big Data, and machine intelligence. The book provides a clear and deep description of algorithms, software systems, data-driven applications, machine learning, and data science concepts, as well as the evolution and impact of artificial intelligence.After introducing computing concepts, the book examines the relationships between algorithms and human work, discussing how jobs are being affected and how computers and software programs are influencing human life and the labor sphere. Topics such as value alignment, collective intelligence, Big Data impact, automatic decision methods, social control, and political uses of algorithms are illustrated and discussed at length without excessive technical detail. Issues related to how corporations, governments, and autocratic regimes are exploiting algorithms and machine intelligence methods to influence people, laws, and markets are extensively addressed. Ethics principles in software programming and human value insertion into artificial intelligence algorithms are also discussed.

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Book SynopsisThis book demonstrates the need for and the value of interdisciplinary research in addressing important societal challenges associated with the widespread use of algorithmic decision-making. Algorithms are increasingly being used to make decisions in various domains such as criminal justice, medicine, and employment. While algorithmic tools have the potential to make decision-making more accurate, consistent, and transparent, they pose serious challenges to societal interests. For example, they can perpetuate discrimination, cause representational harm, and deny opportunities.The Societal Impacts of Algorithmic Decision-Making presents several contributions to the growing body of literature that seeks to respond to these challenges, drawing on techniques and insights from computer science, economics, and law. The author develops tools and frameworks to characterize the impacts of decision-making and incorporates models of behavior to reason about decision-making in complex environments. These technical insights are leveraged to deepen the qualitative understanding of the impacts of algorithms on problem domains including employment and lending.The social harms of algorithmic decision-making are far from being solved. While easy solutions are not presented here, there are actionable insights for those who seek to deploy algorithms responsibly. The research presented within this book will hopefully contribute to broader efforts to safeguard societal values while still taking advantage of the promise of algorithmic decision-making.Table of Contents Introduction Part I: Theoretical Foundations for Fairness in Algorithmic Decision-Making 1. Inherent Tradeoffs in the Fair Determination of Risk Scores 2. On Fairness and Calibration 3. The Externalities of Exploration and How Data Diversity Helps Exploitation Part II: Models of Behavior 4. Selection Problems in the Presence of Implicit Bias 5. How Do Classifiers Induce Agents to Behave Strategically? 6. Algorithmic Monoculture and Social Welfare Part III: Application Domains 7. Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices 8. The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons Part IV: Conclusion and Future Work 9. Future Directions

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Book SynopsisChapter 1: Introduction to NTMA and VQA.- Chapter 2: Network Traffic Monitoring and Analysis.- Chapter 3: Video Quality Assessment.- Chapter 4: Machine Learning Techniques for NTMA and VQA.- Chapter 5: NTMA Application with JavaScript.- Chapter 6: Video Quality Assessment Application Development with JavaScript.- Chapter 7: NTMA and VQA Integration.

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Book SynopsisExplore Generative AI and understand its key concepts, architecture, and tangible business use cases. This book will help you develop the skills needed to use SAP AI Core service features available in the SAP Business Technology Platform. You'll examine large language model (LLM) concepts and gain the practical knowledge to unleash the best use of Gen AI. As you progress, you'll learn how to get started with your own LLM models and work with Generative AI use cases. Additionally, you'll see how to take advantage Amazon Bedrock stack using AWS SDK for ABAP. To fully leverage your knowledge, Generative AI with SAP and Amazon Bedrock offers practical step-by-step instructions for how to establish a cloud SAP BTP account model and create your first GenAIartifacts. This work is an important prerequisite for those who want to take full advantage of generative AI with SAP. What You Will LearnMaster the concepts and terminology of artificial intelligence and GenAIUnderstand opportunities and impacts for different industries with GenAIBecome familiar with SAP AI Core, Amazon Bedrock, AWS SDK for ABAP and develop your firsts GenAI projectsAccelerate your development skillsGain more productivity and time implementing GenAI use casesWho this Book Is ForAnyone who wants to learn about Generative AI for Enterprise and SAP practitioners who want to take advantage of AI within the SAP ecosystem to support their systems and workflows.

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Book SynopsisThis book is yourcomprehensive guide to creating powerful, end-to-end deep learning workflows on Amazon Web Services (AWS). The book explores how to integrate essential big data tools and technologiessuch as PySpark, PyTorch, TensorFlow, Airflow, EC2, and S3to streamline the development, training, and deployment of deep learning models. Starting with the importance of scaling advanced machine learning models, this book leverages AWS's robust infrastructure and comprehensive suite of services. It guides you through the setup and configuration needed to maximize the potential of deep learning technologies. You will gain in-depth knowledge of building deep learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment. The book provides insights into setting up an AWS environment, configuring necessary tools, and using PySpark for distributed data processing. You will also delve into hands-on tutorials for PyTorch and TensorFlow, mastering their roles in building and training neural networks. Additionally, you will learn how Apache Airflow can orchestrate complex workflows and how Amazon S3 and EC2 enhance model deployment at scale. By the end of this book, you will be equipped to tackle real-world challenges and seize opportunities in the rapidly evolving field of deep learning with AWS. You will gain the insights and skills needed to drive innovation and maintain a competitive edge in today's data-driven landscape. What You Will LearnMaximize AWS services for scalable and high-performancedeep learning architecturesHarness the capacity of PyTorch and TensorFlow for advanced neural network developmentUtilize PySpark for efficient distributed data processing onAWSOrchestrate complex workflows withApache Airflow for seamless data processing, model training, and deploymentWho This Book Is ForData scientists looking to expand their skill set to include deep learning on AWS, machine learning engineers tasked with designing and deploying machine learning systems who want to incorporate deep learning capabilities into their applications, AI practitioners working across various industries who seek to leverage deep learning for solving complex problems and gaining a competitive advantage

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Book SynopsisChapter 1: Introduction to Neural Networks.- Chapter 2: Using Tensors.- Chapter 3: How Machines Learn.- Chapter 4: Network Layers.- Chapter 5: The Training Process.- Chapter 6: Generative Models.- Chapter 7: Re-enforcement Learning.- Chapter 8: Using Pre-trained Networks.

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Book SynopsisChapter 1: Introduction to Machine Learning Data Governance.- Chapter 2: Establishing a Data Governance Framework.- Chapter 3: Data Quality and Preprocessing.- Chapter .- 4: Data Privacy and Security Considerations.- Chapter 5: Ethical Implications and Bias Mitigation.- Chapter 6: Model Transparency and Interpretability.- Chapter 7: Monitoring and Maintaining Machine Learning System.- Chapter 8: Regulatory Compliance and Risk Management.- Chapter 9: Organizational Culture and Change Management.- Chapter 10: Future Trends and Emerging Challenges.

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Book SynopsisChapter 1: OI, AI, and Research.- Chapter 2: Neurocognitive Foundations for People Other Than Dr. Rekart.- Chapter 3: All the Feels.- Chapter 4: Being Part of Something.- Chapter 5: Defining the Box.- Chapter 6: Attention (or lack thereof).- Chapter 7: The Evolution and Revolution of People.- Chapter 8: Communication is hard (and we suck at it).- Chapter 9: I remember when - or do I?.- Chapter 10: Making decisions - why we buy lottery tickets.- Chapter 11: Learning (and making mistakes).- Chapter 12: Business, Research, and Design Relationships- It’s Complicated.- Chapter 13: The AI Elephant in the Room.

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Book SynopsisChapter 1: Sorry, But You’re (Probably) Not Using the Best Prompts to Use ChatGPT to Its Highest Potential.- Chapter 2: Prompting ChatGPT to Help You Create New Content and Get Ideas.- Chapter 3: Teaching ChatGPT Information and Using Unique Prompts to Create Content in a Different Style.- Chapter 4: Getting Creative with the ChatGPT Canvas: Prompts to Help You Write Long-Form Content Like an Article or Thesis.- Chapter 5: Prompts to Make Your Life Easier with the Power of ChatGPT’s Data Analysis Abilities.- Chapter 6: Learn New Skills Quickly by Prompting ChatGPT to Act as a Teacher.- Chapter 7: Strategies and Prompts for Your Daily Life: Using ChatGPT as a Personal Assistant.- Chapter 8: Getting Chatty with ChatGPT in a Verbal Conversation.- Chapter 9: Using ChatGPT as a Time-Saver: Prompts Needed to Convert Anything in Your Daily Life.- Chapter 10: Save Yourself from Countless Revisions: Prompts for Using ChatGPT to Rewrite and Rephrase Text.- Chapter 11: Budget Planning, Product Research, and Writing an Article: Always Prompt ChatGPT with Lots of Data!.- Chapter 12: Visualize Your Ideas by Prompting ChatGPT’s DALL-E and Sora.

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