| Description |
1 online resource (307 p.) |
| Contents |
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 |
| Notes |
Description based upon print version of record |
|
Learning Objectives |
| Form |
Electronic book
|
| Author |
Pulari, Sini Raj
|
|
Vasudevan, Subashri
|
| ISBN |
9781000481884 |
|
1000481883 |
|
