DL Book 1 - Understanding Neural Networks and Deep Learning for Absolute Beginners

Table of Contents

1. Introduction

1.1 What is Artificial Intelligence?

1.2 What is Machine Learning?

1.3 What is Deep Learning?

1.4 Why Learn Deep Learning?

1.5 How to Use This Book?

2. Neural Networks Basics

2.1 What is a Neural Network?

2.2 How Does a Neural Network Work?

2.3 Types of Neural Networks

2.4 Neural Network Architectures

2.5 Neural Network Applications

3. Deep Learning Concepts

3.1 What is a Deep Neural Network?

3.2 How Does a Deep Neural Network Learn? 3.3 Activation Functions

3.4 Loss Functions

3.5 Optimization Algorithms

3.6 Regularization Techniques

3.7 Hyperparameter Tuning

4. Deep Learning Models

4.1 Convolutional Neural Networks

4.2 Recurrent Neural Networks

4.3 Long Short-Term Memory Networks

4.4 Gated Recurrent Unit Networks

4.5 Autoencoders

4.6 Generative Adversarial Networks

5. Deep Learning Frameworks

5.1 TensorFlow

5.2 Keras

5.3 PyTorch

5.4 MXNet

5.5 Comparison of Deep Learning Frameworks 6. Deep Learning Projects

6.1 Image Classification

6.2 Object Detection

6.3 Face Recognition

6.4 Natural Language Processing

6.5 Text Generation

6.6 Speech Recognition

6.7 Machine Translation

7. Conclusion

7.1 Summary of Key Points

7.2 Future Trends of Deep Learning

7.3 Resources for Further Learning

7.4 Acknowledgements

7.5 References