Deep Learning: A Comprehensive Guide
- Length: 204 pages
- Edition: 1
- Language: English
- Publisher: Chapman and Hall/CRC
- Publication Date: 2021-11-30
- ISBN-10: 1032028823
- ISBN-13: 9781032028828
- Sales Rank: #0 (See Top 100 Books)
Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning and Machine Learning concepts. Deep Learning and Machine Learning are the most sought-after domains, require a deep understanding and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting from the basics of neural networks, the architecture of various types of CNNs, RNN, LSTM, etc. till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively.
- Includes the smooth transition from ML concepts to DL concepts
- Line by line explanation has been provided for all the coding-based examples
- Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML\DL right away
- Even a person with a non-computer science background can benefit from this book by following the theory, examples, case studies, and code snippets
- Every chapter starts with the objective and ends with a set of quiz questions to test the readers’ understanding
- Includes references to the YouTube videos for providing additional guidance
AI is a domain for everyone. The book is targeted towards everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.
Cover Half Title Title Page Copyright Page Table of Contents Preface The Authors Chapter 1 Introduction to Deep Learning Learning Objectives 1.1 Introduction 1.2 The Need: Why Deep Learning? 1.3 What Is the Need of a Transition From Machine Learning to Deep Learning? 1.4 Deep Learning Applications 1.4.1 Self-Driving Cars 1.4.2 Emotion Detection 1.4.3 Natural Language Processing 1.4.4 Entertainment 1.4.5 Healthcare YouTube Session On Deep Learning Applications Key Points to Remember Quiz Further Reading Chapter 2 The Tools and the Prerequisites Learning Objectives 2.1 Introduction 2.2 The Tools 2.2.1 Python Libraries – Must Know 2.2.2 The Installation Phase A. Anaconda Installation B. Jupyter Installation C. The First Program With the Jupyter D. Keras Installation 2.3 Datasets – A Quick Glance Key Points to Remember Quiz Chapter 3 Machine Learning: The Fundamentals Learning Objectives 3.1 Introduction 3.2 The Definitions – Yet Another Time 3.3 Machine Learning Algorithms 3.3.1 Supervised Learning Algorithms 3.3.2 The Unsupervised Learning Algorithms 3.3.3 Reinforcement Learning 3.3.4 Evolutionary Approach 3.4 How/Why Do We Need ML? 3.5 The ML Framework 3.6 Linear Regression – A Complete Understanding 3.7 Logistic Regression – A Complete Understanding 3.8 Classification – A Must-Know Concept 3.8.1 SVM – Support Vector Machines 3.8.2 K-NN (K-Nearest Neighbor) 3.9 Clustering – An Interesting Concept to Know 3.9.1 K-Means Clustering Key Points to Remember Quiz Further Reading Chapter 4 The Deep Learning Framework Learning Objectives 4.1 Introduction 4.2 Artificial Neuron 4.2.1 Biological Neuron 4.2.2 Perceptron 4.2.2.1 How a Perceptron Works? 4.2.3 Activation Functions 4.2.4 Parameters 4.2.5 Overfitting 4.3 A Few More Terms 4.4 Optimizers Key Points to Remember Quiz Further Reading Chapter 5 CNN – Convolutional Neural Networks: A Complete Understanding Learning Objectives 5.1 Introduction 5.2 What Is Underfitting, Overfitting and Appropriate Fitting? 5.3 Bias/variance – A Quick Learning 5.4 Convolutional Neural Networks 5.4.1 How Convolution Works 5.4.2 How Zero Padding Works 5.4.3 How Max Pooling Works 5.4.4 The CNN Stack – Architecture 5.4.5 What Is the Activation Function? 5.4.5.1 Sigmoid Activation Function 5.4.5.2 ReLU – Rectified Linear Unit 5.4.6 CNN – Model Building – Step By Step Key Points to Remember Quiz Further Reading Chapter 6 CNN Architectures: An Evolution Learning Objectives 6.1 Introduction 6.2 LeNET CNN Architecture 6.3 VGG16 CNN Architecture 6.4 AlexNet CNN Architecture 6.5 Other CNN Architectures at a Glance Key Points to Remember Quiz Further Reading Chapter 7 Recurrent Neural Networks Learning Objectives 7.1 Introduction 7.2 CNN vs. RNN: A Quick Understanding 7.3 RNN vs. Feedforward Neural Networks: A Quick Understanding 7.4 Simple RNN 7.5 LSTM: Long SHORT-TERM Memory 7.6 Gated Recurrent Unit Key Points to Remember Quiz Further Reading Chapter 8 Autoencoders Learning Objectives 8.1 Introduction 8.2 What Is an Autoencoder? 8.2.1 How Autoencoders Work 8.2.2 Properties of Autoencoders 8.3 Applications of Autoencoders 8.3.1 Data Compression and Dimensionality Reduction 8.3.2 Image Denoising 8.3.3 Feature Extraction 8.3.4 Image Generation 8.3.5 Image Colorization 8.4 Types of Autoencoders 8.4.1 Denoising Autoencoder 8.4.2 Vanilla Autoencoder 8.4.3 Deep Autoencoder 8.4.4 Sparse Autoencoder 8.4.5 Undercomplete Autoencoder 8.4.6 Stacked Autoencoder 8.4.7 Variational Autoencoder (VAEs) 8.4.8 Convolutional Autoencoder Key Points to Remember Quiz Further Reading Chapter 9 Generative Models Learning Objectives 9.1 Introduction 9.2 What Is a Generative Model? 9.3 What Are Generative Adversarial Networks (GAN)? 9.4 Types of GAN 9.4.1 Deep Convolutional GANs (DCGANs) 9.4.2 Stack GAN 9.4.3 Cycle GAN 9.4.4 Conditional GAN (CGAN) 9.4.5 Info GAN 9.5 Applications of GAN 9.5.1 Fake Image Generation 9.5.2 Image Modification 9.5.3 Text to Image/Image to Image Generation 9.5.4 Speech Modification 9.5.5 Assisting Artists 9.6 Implementation of GAN Key Points to Remember Quiz Further Reading Chapter 10 Transfer Learning Learning Objectives 10.1 What Is Transfer Learning? 10.2 When Can We Use Transfer Learning? 10.3 Example – 1: Cat Or Dog Using Transfer Learning With VGG 10.4 Example – 2: Identify Your Relatives’ Faces Using Transfer Learning 10.5 The Difference Between Transfer Learning and Fine Tuning 10.6 Transfer Learning Strategies 10.6.1 Same Domain, Task 10.6.2 Same Domain, Different Task 10.6.3 Different Domain, Same Task Key Points to Remember Quiz Further Reading Chapter 11 Intel OpenVino: A Must-Know Deep Learning Toolkit Learning Objectives 11.1 Introduction 11.2 OpenVino Installation Guidelines Key Points to Remember Quiz Further Reading Chapter 12 Interview Questions and Answers Learning Objectives YouTube Sessions On Deep Learning Applications Index
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