Neural Networks and architectures

Anshuman Mishra

This audiobook is narrated by a digital voice. Artificial Intelligence (AI) has emerged as one of the most transformative and revolutionary fields in modern science and technology. From self-driving cars to intelligent personal assistants, from...

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Preface vi-xii

1/24/2026

Chapter 1: Introduction to Neural Networks 1-41

1/24/2026

Chapter 2: Mathematical Foundations 42-80

1/24/2026

Chapter 3: Perceptron and Multi-Layer Perceptron (MLP) 81-109

1/24/2026

Chapter 4: Activation Functions and Regularization 110-144

1/24/2026

Chapter 5: Convolutional Neural Networks (CNNs) 145-181

1/24/2026

Chapter 6: Recurrent Neural Networks (RNNs) 182-220

1/24/2026

Chapter 7: Autoencoders and Generative Models 221-241

1/24/2026

Chapter 8: Advanced Neural Network Architectures 242-274

1/24/2026

Chapter 9: Optimization Techniques 275-298

1/24/2026

Chapter 10: Practical Implementation 299-340

1/24/2026

Chapter 11: Challenges and Future Directions 341-367

1/24/2026

Preface

1/24/2026

Introduction to the Book

1/24/2026

Importance of Neural Networks in Modern AI

1/24/2026

How the Book is Structured for Learning

1/24/2026

Target Audience

1/24/2026

Learning Outcomes

1/24/2026

Why This Book is Important

1/24/2026

Chapter 1: Introduction to Neural Networks

1/24/2026

1.2 History and Evolution of Neural Networks

1/24/2026

1.3 Biological Inspiration: Neurons and Brain Structure

1/24/2026

1.4 Applications of Neural Networks in Real Life

1/24/2026

1.5 Limitations of Neural Networks

1/24/2026

Chapter 2: Mathematical Foundations

1/24/2026

2.2 Calculus Essentials (Derivatives, Gradients)

1/24/2026

2.3 Probability and Statistics Basics

1/24/2026

2.4 Activation Functions and Non-linearity

1/24/2026

2.5 Loss Functions and Error Metrics

1/24/2026

Chapter 3: Perceptron and Multi-Layer Perceptron (MLP)

1/24/2026

Chapter 4: Activation Functions and Regularization

1/24/2026

Step 1: Calculate the exponential of each logit.

1/24/2026

Chapter 5: Convolutional Neural Networks (CNNs)

1/24/2026

Step 1: First 2x2 window (top-left)

1/24/2026

Step 2: Slide 2 steps to the right (due to stride = 2)

1/24/2026

Step 3: Slide 2 steps down (from the initial position)

1/24/2026

Step 4: Slide 2 steps to the right (from the last position)

1/24/2026

Step 2: Slide 2 steps to the right

1/24/2026

Step 3: Slide 2 steps down

1/24/2026

Step 4: Slide 2 steps to the right

1/24/2026

Chapter 6: Recurrent Neural Networks (RNNs)

1/24/2026

Part 1: Input Gate Layer (it)

1/24/2026

Part 2: Candidate Cell State (C~t)

1/24/2026

Part 1: Output Gate Layer (ot)

1/24/2026

Part 2: New Hidden State (ht)

1/24/2026

Chapter 7: Autoencoders and Generative Models

1/24/2026

Chapter 8: Advanced Neural Network Architectures

1/24/2026

Chapter 9: Optimization Techniques

1/24/2026

Chapter 10: Practical Implementation

1/24/2026

Chapter 11: Challenges and Future Directions

1/24/2026

Neural Networks and architectures

Anshuman Mishra

This audiobook is narrated by a digital voice. Artificial Intelligence (AI) has emerged as one of the most transformative and revolutionary fields in modern science and technology. From self-driving cars to intelligent personal assistants, from...

Preface vi-xii

Chapter 1: Introduction to Neural Networks 1-41

Chapter 2: Mathematical Foundations 42-80

Chapter 3: Perceptron and Multi-Layer Perceptron (MLP) 81-109

Chapter 4: Activation Functions and Regularization 110-144

Chapter 5: Convolutional Neural Networks (CNNs) 145-181

Chapter 6: Recurrent Neural Networks (RNNs) 182-220

Chapter 7: Autoencoders and Generative Models 221-241

Chapter 8: Advanced Neural Network Architectures 242-274

Chapter 9: Optimization Techniques 275-298

Chapter 10: Practical Implementation 299-340

Chapter 11: Challenges and Future Directions 341-367

Preface

Introduction to the Book

Importance of Neural Networks in Modern AI

How the Book is Structured for Learning

Target Audience

Learning Outcomes

Why This Book is Important

Chapter 1: Introduction to Neural Networks

1.2 History and Evolution of Neural Networks

1.3 Biological Inspiration: Neurons and Brain Structure

1.4 Applications of Neural Networks in Real Life

1.5 Limitations of Neural Networks

Chapter 2: Mathematical Foundations

2.2 Calculus Essentials (Derivatives, Gradients)

2.3 Probability and Statistics Basics

2.4 Activation Functions and Non-linearity

2.5 Loss Functions and Error Metrics

Chapter 3: Perceptron and Multi-Layer Perceptron (MLP)

Chapter 4: Activation Functions and Regularization

Step 1: Calculate the exponential of each logit.

Chapter 5: Convolutional Neural Networks (CNNs)

Step 1: First 2x2 window (top-left)

Step 2: Slide 2 steps to the right (due to stride = 2)

Step 3: Slide 2 steps down (from the initial position)

Step 4: Slide 2 steps to the right (from the last position)

Step 2: Slide 2 steps to the right

Step 3: Slide 2 steps down

Step 4: Slide 2 steps to the right

Chapter 6: Recurrent Neural Networks (RNNs)

Part 1: Input Gate Layer (it​)

Part 2: Candidate Cell State (C~t​)

Part 1: Output Gate Layer (ot​)

Part 2: New Hidden State (ht​)

Chapter 7: Autoencoders and Generative Models

Chapter 8: Advanced Neural Network Architectures

Chapter 9: Optimization Techniques

Chapter 10: Practical Implementation

Chapter 11: Challenges and Future Directions

Part 1: Input Gate Layer (it)

Part 2: Candidate Cell State (C~t)

Part 1: Output Gate Layer (ot)

Part 2: New Hidden State (ht)