Machine Learning: Concepts, Techniques and Applications
- Length: 442 pages
- Edition: 1
- Language: English
- Publisher: Chapman and Hall/CRC
- Publication Date: 2023-05-18
- ISBN-10: 103226828X
- ISBN-13: 9781032268286
- Sales Rank: #0 (See Top 100 Books)
Machine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding.
Features
- Concepts of Machine learning from basics to algorithms to implementation
- Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers
- Machine Learning from an Application Perspective – General & Machine learning for Healthcare, Education, Business, Engineering Applications
- Ethics of machine learning including Bias, Fairness, Trust, Responsibility
- Basics of Deep learning, important deep learning models and applications
- Plenty of objective questions, Use Cases, Activity and Project based Learning Exercises
The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students, researchers and professionals so that they can formulate the problems, prepare data, decide features, select appropriate machine learning algorithms and do appropriate performance evaluation.
Cover Half Title Title Page Copyright Page Table of Contents Preface Author Biography Chapter 1: Introduction 1.1 Introduction 1.1.1 Intelligence 1.1.2 Learning 1.1.3 Informal Introduction to Machine Learning 1.1.4 Artificial Intelligence, Machine Learning, and Deep Learning 1.2 Need for Machine Learning 1.2.1 Extracting Relevant Information 1.2.2 Why Study Machine Learning? 1.2.3 Importance of Machine Learning 1.3 Machine Learning—Road Map 1.3.1 Early History 1.3.2 Focus on Neural Networks 1.3.3 Discovery of Foundational Ideas of Machine Learning 1.3.4 Machine Learning from Knowledge-Driven to Data-Driven 1.3.5 Applied Machine Learning—Text and Vision and Machine Learning Competitions 1.3.6 Deep Learning—Coming of Age of Neural Nets 1.3.7 Industrial Participation and Machine Learning 1.4 What Is Machine Learning? 1.5 Explaining Machine Learning 1.6 Areas of Influence for Machine Learning 1.7 Applications of Machine Learning 1.7.1 Applications of Machine Learning Across the Spectrum 1.7.2 Machine Learning in the Big Data Era 1.7.3 Interesting Applications 1.8 Identifying Problems Suitable for Machine Learning 1.9 Advantages of Machine Learning 1.10 Disadvantages of Machine Learning 1.11 Challenges of Machine Learning 1.12 Summary 1.13 Points to Ponder E.1.3 Match the Columns E.1.4 Sequencing References Chapter 2: Understanding Machine Learning 2.1 General Architecture of a Machine Learning System 2.2 Machine Learning Terminology 2.3 Types of Machine Learning Tasks 2.4 Design of a Simple Machine Learning System 2.4.1 Important Aspects in the Design of a Learning System 2.4.2 Illustrative Examples of the Process of Design 2.4.2.1 Recognition of Handwritten Characters 2.4.2.2 Checkers Learning 2.5 Summary 2.6 Points to Ponder E.2 Exercises E.2.1 Suggested Activities Self-Assessment Questions E.2.2 Multiple Choice Questions E.2.3 Match the Columns E.2.4 Short Questions References Chapter 3: Mathematical Foundations and Machine Learning 3.1 Introduction 3.2 What Is Linear Algebra? 3.2.1 Linear Algebra and Machine Learning 3.2.2 Matrix and Matrix Operations 3.2.2.1 Vector and Vector Operations 3.2.2.2 Operations with Vectors 3.2.2.3 Linear Dependence and Independence 3.2.2.3.1 Vector Projection 3.3 Probability Theory 3.3.1 Machine Learning and Probability Theory 3.3.2 Basics of Probability 3.3.3 Three Approaches to Probability 3.3.4 Types of Events in Probability 3.3.4.1 Visualizing Events in Sample Space 3.3.5 Probability of Multiple Random Variables 3.3.5.1 Simple Versus Joint Probability 3.3.6 Marginalization 3.3.7 Conditional Probability and Bayes Theorem 3.3.7.1 Bayes Theorem 3.3.7.2 Example 3.7 Bayes Theorem 3.3.7.3 Example 3.8 Bayes Theorem 3.3.8 Bayes Theorem and Machine Learning 3.3.9 Probability of Continuous Variables for Modelling the World 3.3.9.1 Characteristics of a Normal Probability Distribution 3.3.9.2 Standard Normal Probability Distribution 3.3.10 Use Case—Bayes Theorem—Diagnostic Test Scenario 3.4 Information Theory 3.4.1 Shannon’s Fundamental Theorems 3.4.1.1 Information Source 3.4.1.2 Stochastic Sources 3.4.2 Self Information 3.4.3 Entropy 3.4.4 Entropy for Memory and Markov Sources 3.4.4.1 The Source Coding Theorem 3.4.5 Cross Entropy 3.4.6 Kullback–Leibler Divergence or Relative Entropy 3.5 Summary 3.6 Points to Ponder E.3 Exercises E.3.1 Suggested Activities Case study Self-Assessment Questions E.3.2 Multiple Choice Questions E.3.3 Match the Columns E.3.4 Problems E.3.5 Short Questions Chapter 4: Foundations and Categories of Machine Learning Techniques 4.1 Introduction 4.1.1 Data and Its Importance 4.1.2 Problem Dimensions 4.2 Data and Data Representation 4.2.1 Types of Data 4.2.2 Data Dependencies 4.2.3 Data Representation 4.2.4 Processing Data 4.2.5 Data Biases 4.2.6 Features and Feature Selection 4.2.6.1 Why Feature Selection? 4.2.6.2 Approaches to Feature Selection 4.2.6.3 Feature Extraction 4.3 Basis of Machine Learning 4.3.1 Inductive Learning 4.3.2 Generalization 4.3.3 Bias and Variance 4.3.4 Overfitting and Underfitting 4.3.4.1 Overfitting 4.3.4.1.1 Methods to Avoid Overfitting 4.3.4.2 Underfitting 4.4 Issues in Building Machine Learning Models 4.5 Offline and Online Machine Learning 4.6 Underlying Concepts of Machine Learning Algorithms—Parametric and Nonparametric Algorithms 4.6.1 Parametric Learning Versus Nonparametric 4.7 Approaches to Machine Learning Algorithms 4.8 Types of Machine Learning Algorithms 4.8.1 Supervised Learning 4.8.1.1 Workflow of a Supervised Learning System 4.8.1.2 Classification and Regression 4.8.1.3 Examples of Supervised Learning 4.8.2 Unsupervised Learning 4.8.2.1 Workflow of Unsupervised Learning System 4.8.2.2 Clustering, Association Rule Mining, and Dimensionality Reduction 4.8.3 Semi-supervised Learning 4.8.4 Reinforcement Learning 4.8.5 Neural Networks and Deep Learning 4.9 Summary 4.10 Points to Ponder E.4 Exercises E.4.1 Suggested Activities Use Case Thinking Exercise Self-Assessment Questions E.4.2 Multiple Choice Questions E.4.3 Match the Columns E.4.4 Short Questions Chapter 5: Machine Learning: Tools and Software 5.1 Weka Tool 5.1.1 Features of Weka 5.1.2 Installation of Weka 5.1.3 Weka Application Interfaces 5.1.4 Data Formats of Weka 5.1.5 Weka Explorer 5.1.6 Data Preprocessing 5.1.7 Understanding Data 5.1.7.1 Selecting Attributes 5.1.7.2 Removing Attributes 5.1.7.3 Applying Filters 5.2 Introduction to Machine Learning Support in R 5.2.1 Advantages to Implement Machine Learning Using R Language 5.2.2 Popular R Language Packages 5.2.3 Application of R in Machine Learning 5.2.4 Examples of Machine Learning Problems 5.2.5 Types of Machine Learning Problems 5.2.6 Setting Up Environment for Machine Learning with R Programming Using Anaconda 5.2.7 Running R Commands 5.2.8 Installing Machine Learning Packages in R 5.2.9 Specific Machine Learning Packages in R 5.2.10 Supervised Learning Using R 5.2.11 Unsupervised Learning Using R 5.2.11.1 Implementing k - Means Clustering in R 5.3 Summary 5.4 Points to Ponder E.5 Exercises E.5.1. Suggested Activities Self-Assessment Questions E.5.2 Multiple Choice Questions E.5.3 Match the Columns E.5.4 Short Questions Chapter 6: Classification Algorithms 6.1 Introduction 6.1.1 Basic Concepts of Classification 6.1.2 Binary Classification 6.1.3 Multi-Class Classification 6.1.4 Multi-Label Classification for Machine Learning 6.2 Decision Based Methods—Nonlinear Instance-Based Methods— k- Nearest Neighbor 6.2.1 Introduction 6.2.2 Need for KNN 6.2.3 Working of KNN 6.2.4 Calculating the Distance 6.2.5 Two Parameters of KNN 6.2.6 KNN Algorithm 6.2.7 Pros and Cons of KNN 6.3 Decision Based Methods—Decision Tree Algorithm 6.3.1 Decision Tree Based Algorithm 6.3.2 Terminology Associated with Decision Trees 6.3.3 Assumptions While Creating Decision Tree 6.3.4 How Do Decision Trees Work? 6.3.5 Steps in ID3 Algorithm 6.3.6 Attribute Selection Measures 6.3.6.1 Entropy 6.3.6.2 Gini Impurity 6.3.6.3 Information Gain 6.3.6.3.1 Information Gain—An Example 6.3.6.4 Calculation of the Entropies 6.3.6.5 Computing the Gain 6.3.6.6 Information Gain Versus Gini Impurity 6.4 Linear Models—Support Vector Machines 6.4.1 Nonlinear Data 6.4.2 Working of SVM 6.4.3 Important Concepts in SVM 6.4.3.1 Support Vectors 6.4.3.2 Hard Margin 6.4.3.3 Soft Margin 6.4.3.4 Different Kernels 6.4.4 Tuning Parameters 6.4.5 Advantages and Disadvantages of SVM 6.5 Use Cases 6.5.1 Healthcare Industries 6.5.2 Banking Sectors 6.5.3 Educational Sectors 6.6 Summary 6.7 Points to Ponder E.6 Exercises E.6.1 Suggested Activities E.6.2 Self-Assessment Questions E.6.3 Multiple Choice Questions E.6.4 Match the Columns E.6.5 Short Questions Chapter 7: Probabilistic and Regression Based Approaches 7.1 Probabilistic Methods 7.1.1 Introduction—Bayes Learning 7.1.2 Bayesian Learning 7.1.3 Interpretation of Bayes Rule 7.1.4 Benefits of Bayesian Learning 7.1.5 Problems with Bayesian Learning 7.2 Algorithms Based on Bayes Framework 7.2.1 Choosing Hypotheses 7.2.2 Bayesian Classification 7.2.2.1 Discriminative Model 7.2.2.2 Generative Model 7.3 Naïve Bayes Classifier 7.3.1 Naïve Bayes Classifier: Assumptions 7.3.2 Naïve Bayes Algorithm 7.3.3 Characteristics of Naïve Bayes 7.3.4 Naïve Bayes Classification—Example 1—Corona Dataset 7.4 Bayesian Networks 7.4.1 Foundations of Bayesian Network 7.4.2 Bayesian Network Perspectives 7.4.3 Bayesian Network—Probability Fundamentals 7.4.4 Semantics of Bayesian Networks 7.4.5 Bayesian Network—Putting It All Together 7.4.5.1 Independence 7.4.5.2 Putting It Together 7.4.6 Limitations of Bayesian Networks 7.4.7 Constructing Bayesian Networks 7.4.8 Bayesian Networks—Eating Habits 7.4.9 Causes and Bayes’ Rule 7.4.10 Conditional Independence in BNs 7.4.11 Bayesian Networks—Alarm (from Judea Pearl)— Example 7.4.11.1 Semantics of Bayesian Networks—Alarm Network 7.4.11.2 Inferences in Bayesian Networks—Alarm Network 7.5 Regression Methods 7.5.1 Linear Regression Models 7.5.1.1 Steps in Learning a Linear Regression Model 7.5.1.1.1 Choosing the Model 7.5.1.1.2 Defining Loss Function 7.5.1.1.3 Controlling Model Complexity and Overfitting 7.5.1.1.4 Fitting or Optimizing the Model—Gradient Descent 7.5.2 Logistic Regression 7.6 Summary 7.7 Points to Ponder E.7 Exercise E.7.1 Suggested Activities Self-Assessment Questions E.7.2 Multiple Choice Questions E.7.3 Match the Columns E.7.4 Problems E.7.5 Short Questions References Chapter 8: Performance Evaluation and Ensemble Methods 8.1 Introduction 8.2 Classification Metrics 8.2.1 Binary Classification 8.2.1.1 Accuracy 8.2.1.2 Sensitivity or True Positive Rate 8.2.1.3 Precision 8.2.1.4 Precision/Recall Trade-off 8.2.1.5 F1 Score 8.2.1.6 ROC/AUC Curve 8.2.2 Multi-Class Classification 8.3 Cross-Validation in Machine Learning 8.4 Ensemble Methods 8.4.1 Types of Ensemble Methods 8.4.1.1 Bagging 8.4.1.2 Comparison of Bagging and Boosting 8.4.1.3 Stacking 8.5 Handling Missing and Imbalanced Data 8.6 Summary 8.7 Points to Ponder E.8 Exercises E.8.1 Suggested Activities Self-Assessment Questions E.8.2 Multiple Choice Questions E.8.3 Match the Columns E.8.4 Short Questions References Chapter 9: Unsupervised Learning 9.1 Introduction to Unsupervised Learning 9.1.1 Importance of Unsupervised Learning 9.1.2 Challenges of Unsupervised Learning 9.2 Applications of Unsupervised Learning 9.3 Supervised Learning Versus Unsupervised Learning 9.4 Unsupervised Learning Approaches 9.5 Clustering 9.5.1 Clusters—Distance Viewpoint 9.5.2 Applications of Clustering 9.6 Similarity Measures 9.6.1 Distance Functions for Numeric Attributes 9.6.2 Distance Functions for Binary Attributes 9.7 Methods of Clustering 9.7.1 Hierarchical Clustering 9.7.2 Types of Hierarchical Clustering 9.7.2.1 Agglomerative Clustering 9.7.2.2 Divisive Clustering 9.7.3 Hierarchical Clustering: The Algorithm 9.7.3.1 Hierarchical Clustering: Merging Clusters 9.8 Agglomerative Algorithm 9.9 Issues Associated with Hierarchical Clustering 9.10 Partitional Algorithm 9.11 k - Means Clustering 9.11.1 Steps of k - Means 9.11.2 Issues Associated with the k - Means Algorithm 9.11.3 Evaluating k - Means Clusters 9.11.4 Strengths and Weaknesses of k - Means 9.12 Cluster Validity 9.12.1 Comparing with Ground Truth 9.12.2 Purity Based Measures 9.12.3 Internal Measure 9.13 Curse of Dimensionality 9.14 Dimensionality Reduction 9.15 The Process of Dimensionality Reduction 9.15.1 Criterion for Reduction 9.15.2 Feature Reduction and Feature Selection 9.16 Dimensionality Reduction with Feature Reduction 9.16.1 Principle Component Analysis (PCA) 9.16.1.1 PCA Methodology 9.16.2 Fisher Linear Discriminant Approach 9.16.2.1 Fisher Linear Discriminant 9.16.3 Singular Value Decomposition 9.17 Association Rule Mining 9.17.1 Market-Basket Analysis 9.17.2 Association Rule Mining—Basic Concepts 9.17.3 Apriori Algorithm 9.17.3.1 Apriori Principle 9.17.3.2 The Algorithm 9.17.3.3 Example of Frequent Itemset Generation Using Apriori Algorithm 9.17.3.4 Improving Apriori’s Efficiency 9.18 Summary 9.19 Points to Ponder E.9 Exercises E.9.1 Suggested Activities Self-Assessment Questions E.9.2 Multiple Choice Questions E.9.3 Match the Columns E.9.4 Problems E.9.5 Short Questions References Chapter 10: Sequence Models 10.1 Sequence Models 10.2 Applications of Sequence Models 10.2.1 Examples of Applications with Sequence Data 10.2.2 Examples of Application Scenario of Sequence Models 10.3 Modelling Sequence Learning Problems 10.4 Markov Models 10.4.1 Types of Markov Models 10.4.2 Markov Chain Model 10.4.3 Hidden Markov Model (HMM) 10.4.3.1 Parameters of an HMM 10.4.3.2 The Three Problems of HMM 10.4.4 Markov Decision Process 10.4.5 Partially Observable Markov Decision Process 10.4.6 Markov Random Field 10.5 Data Stream Mining 10.5.1 Challenges in Data Stream Mining 10.6 Learning from Stream Data 10.6.1 Tree Based Learning 10.6.2 Adaptive Learning Using Naïve Bayes 10.6.3 Window Management Models 10.6.4 Data Stream Clustering 10.7 Applications 10.7.1 Applications of Markov Model 10.7.2 Applications of Stream Data Processing 10.7.2.1 Fraud and Anomaly Detection 10.7.2.2 Internet of Things (IoT) Edge Analytics 10.7.2.3 Customization for Marketing and Advertising 10.8 Summary 10.9 Points to Ponder E.10 Exercises E.10.1 Suggested Activities Self-Assessment Questions E.10.2 Multiple Choice Questions E.10.3 Match the Columns E.10.4 Problems E.10.5 Short Questions Chapter 11: Reinforcement Learning 11.1 Introduction 11.2 Action Selection Policies 11.3 Finite Markov Decision Processes 11.4 Problem Solving Methods 11.4.1 Dynamic Programming 11.4.2 Monte Carlo Methods 11.4.2.1 Monte Carlo Reinforcement Learning 11.4.2.2 Finding the Optimal Policy Using Monte Carlo 11.4.2.3 Monte Carlo with Fresh Exploration 11.4.2.4 Monte Carlo with Continual Exploration 11.4.3 Temporal Difference Learning 11.4.3.1 Q-learning 11.4.3.2 State–Action–Reward–State–Action (SARSA) 11.4.3.3 Deep Q-Networks (DQN) 11.5 Asynchronous Reinforcement Learning 11.5.1 Asynchronous Reinforcement Learning Procedure 11.6 Summary 11.7 Points to Ponder E.11 Exercises E.11.1 Suggested Activities Self-Assessment Questions E.11.2 Multiple Choice Questions E.11.3 Match the Columns E.11.4 Short Questions Chapter 12: Machine Learning Applications: Approaches 12.1 Introduction 12.2 Machine Learning Life Cycle 12.3 Choosing a Machine Learning Algorithm for a Problem 12.4 Machine Learning and Its Applications 12.5 Machine Learning for Natural Language Processing 12.6 Recommendation Systems 12.6.1 Basics of Recommendation Systems 12.6.2 Utility Matrix and Recommendation Systems 12.6.3 Issues Associated with Recommendation Systems 12.6.3.1 Construction of Utility Matrix 12.6.3.2 The Cold Start Problem 12.6.3.3 Data Sparsity 12.6.3.4 Inability to Capture Changing User Behaviour 12.6.3.5 Other Issues 12.6.4 Types of Recommendation Algorithms 12.6.5 Collaborative Filtering: Memory Based Methods 12.6.5.1 Collaborative Filtering—Neighbor Based Algorithm 12.6.5.2 User Based Collaborative Filtering 12.6.5.3 Item Based Collaborative Filtering 12.6.5.4 Advantages and Disadvantages of Collaborative Based Filtering 12.6.6 Content Based Recommendation 12.6.6.1 Advantages and Disadvantages of Content Based Recommendation 12.7 Context Aware Recommendations 12.8 Summary 12.9 Points to Ponder E.12 Exercises E.12.1 Suggested Activities Use case Thinking Exercise Self-Assessment Questions E.12.2 Multiple Choice Questions E.12.3 Match the Columns E.12.4 Short Questions Chapter 13: Domain Based Machine Learning Applications 13.1 Introduction 13.2 Everyday Examples of Machine Learning Applications 13.2.1 Personal Smart Assistants 13.2.2 Product Recommendation 13.2.3 Email Intelligence 13.2.4 Social Networking 13.2.5 Commuting Services 13.3 Machine Learning in Health Care 13.3.1 Why Machine Learning for Health Care Now? 13.3.2 What Makes Health Care Different? 13.3.3 Disease Identification and Diagnosis 13.3.4 Medical Imaging and Diagnosis 13.3.5 Smart Health Records 13.3.6 Drug Discovery and Manufacturing 13.3.7 Personalized Medicine and Treatment 13.3.8 Disease Outbreak Prediction 13.4 Machine Learning for Education 13.4.1 Personalized and Adaptive Learning 13.4.2 Increasing Efficiency 13.4.3 Learning Analytics 13.4.4 Predicative Analytics 13.4.5 Evaluating Assessments 13.5 Machine Learning in Business 13.5.1 Customer Service 13.5.2 Financial Management 13.5.3 Marketing 13.5.4 Consumer Convenience 13.5.5 Human Resource Management 13.6 Machine Learning in Engineering Applications 13.6.1 Manufacturing 13.6.2 Water and Energy Management 13.6.3 Environment Engineering 13.7 Smart City Applications 13.8 Summary 13.9 Points to Ponder E.13 Exercises E.13.1 Suggested Activities Self-Assessment Questions E.13.2 Multiple Choice Questions E.13.3 Questions Chapter 14: Ethical Aspects of Machine Learning 14.1 Introduction 14.2 Machine Learning as a Prediction Model 14.3 Ethics and Ethical Issues 14.3.1 Examples of Ethical Concerns 14.4 Ethics and Machine Learning 14.4.1 Opinions of Ethics in AI and Machine Learning 14.5 Fundamental Concepts of Responsible and Ethical Machine Learning 14.5.1 Current Legislation—General Data Protection Regulation (GDPR) 14.6 Fairness and Machine Learning 14.7 Bias and Discrimination 14.7.1 Examples of Bias and Dealing with Bias 14.7.1.1 Bias in Face Recognition Systems 14.7.1.2 Gender-Biased Issues in Natural Language Processing 14.7.1.3 Credit Score Computation 14.7.1.4 User Profiling and Personalization 14.7.2 Types of Bias 14.7.3 Data Bias and the Data Analysis Pipeline 14.8 Fairness Testing 14.8.1 Fairness and Evaluation Metrics 14.9 Case Study: LinkedIn Representative Talent Search 14.10 Explainability 14.10.1 Handling Black Box Machine Learning 14.10.2 Achieving Explainable Machine Learning 14.10.2.1 Attribution Methods 14.10.2.2 Other Methods for Explainability 14.10.3 Example: LinkedIn’s Approach to Explainability 14.11 Transparency 14.11.1 Issues of Transparency and Their Mitigation 14.12 Privacy 14.12.1 Data Privacy 14.12.2 Privacy Attacks 14.12.3 Privacy-Preserving Techniques 14.13 Summary 14.14 Points to Ponder E.14 Exercises E.14.1 Suggested Activities Self-Assessment Questions E.14.2 Multiple Choice Questions E.14.3 Match the Columns E.14.4 Short Questions References Chapter 15: Introduction to Deep Learning and Convolutional Neural Networks 15.1 Introduction 15.2 Example of Deep Learning at Work 15.3 Evolution of Deep Learning 15.4 Deep Learning in Action 15.4.1 Applications 15.4.2 Differences Between Machine Learning and Deep Learning 15.5 Neural Networks 15.5.1 Basic Components of Biological Neurons 15.5.1.1 Perceptrons 15.5.1.2 Perceptron Training 15.5.2 Activation Functions 15.6 Learning Algorithm 15.6.1 Backpropagation 15.6.2 Chain Rule 15.6.3 Backpropagation—For Outermost Layer 15.6.4 Backpropagation—For Hidden Layer 15.7 Multilayered Perceptron 15.7.1 The Basic Structure 15.7.2 Backpropagation 15.8 Convolutional Neural Network 15.8.1 Biological Connection to CNN 15.8.2 The Architecture of Convolutional Neural Networks (CNNs) 15.8.3 Convolutional Networks in the History of Deep Learning 15.8.4 Learning in Convolutional Neural Networks 15.8.4.1 Image Channels 15.8.4.2 Convolution 15.8.4.3 Pooling 15.8.4.4 Flattening 15.8.4.5 Full Connection: A Simple Convolutional Network 15.9 Summary 15.10 Points to Ponder E.15 Exercises E.15.1 Suggested Activities Self-Assessment Questions E.15.2 Multiple Choice Questions E.15.3 Match the Columns E.15.4 Short Questions Chapter 16: Other Models of Deep Learning and Applications of Deep Learning 16.1 Recurrent Neural Networks (RNNs) 16.1.1 Working of RNN 16.1.2 Training Through RNN 16.2 Auto-encoders 16.2.1 Different Types of Auto-encoders 16.3 Long Short Term Memory Networks 16.3.1 How Does LSTM Solve the Problem of Vanishing and Exploring Gradients? 16.4 Generative Adversarial Networks (GANs) 16.4.1 Idea Behind GAN 16.4.2 Generative Versus Discriminative Models 16.5 Radial Basis Function Neural Network 16.5.1 Components of RBNN 16.6 Multilayer Perceptrons (MLPs) 16.6.1 Layers 16.6.2 Learning in MLP 16.6.3 Applications of MLPs 16.6.3.1 RBNN and MLP 16.6.3.2 Comparing RBNN and MLP 16.7 Self-Organizing Maps (SOMs) 16.7.1 Architecture SOM 16.7.2 Working of SOM 16.7.2.1 An Illustration of the Training of a Self-Organizing Map 16.7.3 Options for Initialization 16.7.3.1 Drawbacks to Kohonen Maps 16.8 Restricted Boltzmann Machines (RBMs) 16.8.1 Working of Restricted Boltzmann Machine 16.8.2 Advantages and Drawbacks of RBM 16.9 Deep Belief Networks (DBNs) 16.9.1 Creation of a Deep Belief Network 16.9.2 Benefits and Drawbacks of Deep Belief Networks 16.9.2.1 Benefits 16.9.2.2 Drawbacks 16.9.2.3 Applications of DBN 16.10 Applications 16.10.1 Deep Learning for Vision 16.10.2 Deep Learning for Drug Discovery 16.10.3 Deep Learning for Business and Social Network Analytics 16.11 Summary 16.12 Points to Ponder E.16 Exercises E.16.1 Suggested Activities Self-Assessment Questions E.16.2 Multiple Choice Questions E.16.3 Match the Columns E.16.4 Short Questions References A1: Solutions Chapter – 1 Chapter – 2 Chapter – 3 Chapter – 4 Chapter – 5 Chapter – 6 Chapter – 7 Chapter – 8 Chapter – 9 Chapter – 10 Chapter – 11 Chapter – 12 Chapter – 13 Chapter – 14 Chapter – 15 Chapter – 16 Index
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