Mastering Neural Networks: A Comprehensive Guide to Learn Neural Networks

Table of contents :
1. Preface
1.1. Introduction
1.2. How to Use This Book
2. Introduction to Neural Networks
2.1. What Are Neural Networks?
2.2. Historical Background
2.3. Applications
3. Mathematical Basics
3.1. Linear Algebra
3.2. Calculus
3.3. Probability and Statistics
4. Programming Prerequisites
4.1. Python for Machine Learning
4.2. Libraries and Frameworks
4.3. Setting Up Your Development Environment
5. Fundamental Concepts
5.1 Nodes and Layers
5.2 Activation Functions
5.3 Loss Functions
5.4 Backpropagation
6. Data Representation
6.1 Vectors, Matrices, and Tensors: The Building Blocks of Data Representation
6.2 Data Preprocessing: The Unseen Hero of Machine Learning Pipelines
6.3 Feature Engineering: The Art of Extracting Signals from Data
7. Learning and Optimization
7.1 Gradient Descent
7.2 Learning Rate
7.3 Regularization Methods
8. Perceptrons
8.1 Single-layer Perceptrons
8.2 Multi-layer Perceptrons
9. Feedforward Neural Networks
9.1 Architecture of Feedforward Neural Networks
9.2 Training and Learning in Feedforward Neural Networks
9.3 Use-cases and Applications of Feedforward Neural Networks
10. Training Techniques
10.1 Batch and Online Learning
10.2 Adaptive Learning Rates
10.3 Early Stopping
11. Evaluation and Metrics
11.1. Accuracy, Precision, and Recall
11.2. Confusion Matrix
11.3. ROC and AUC
12.1 What is Deep Learning?
12.2 History and Milestones
13. Convolutional Neural Networks (CNNs)
13.1. Convolutional Layers
13.2. Pooling and Normalization
13.3. Applications in Computer Vision
14. Recurrent Neural Networks (RNNs)
14.1. LSTM and GRU Cells
14.2. Sequence-to-Sequence Models
14.3. Applications in Natural Language Processing
15. Generative Models
15.1 Autoencoders
15.2 Generative Adversarial Networks (GANs)
16. Transfer Learning
16.1. What is Transfer Learning?
16.2. Fine-tuning Pretrained Models
17. Attention Mechanisms
17.1. Understanding Attention
17.2. Transformers: The Pioneers of Attention in Modern Neural Networks
18. Graph Neural Networks: Learning from Irregular Structured Data
18.1 Basics of Graph Theory
18.2 Graph Convolution
19. Reinforcement Learning with Neural Networks
19.1. Markov Decision Processes: The Mathematical Backbone of Reinforcement Learning
19.2. Policy and Value Networks: Driving Decision-making in Reinforcement Learning
20. Hybrid Models: Bridging the Gap between Machine Learning Paradigms
20.1 Neuro-Fuzzy Systems: A Synergy of Neural Networks and Fuzzy Logic
20.2 Evolutionary Algorithms: Mimicking Nature's Ingenuity in Computation
21. Optimization Techniques
21.1 Momentum and RMSprop: Gearing Up Your Optimization Strategies
21.2 Adam and Nadam: A Deep Dive into Advanced Optimization Techniques
22. Scaling Neural Networks
22.1. Distributed Computing for Neural Networks
22.2 Hardware Acceleration in Neural Networks
23. Ethics in Neural Networks
23.1 Bias and Fairness in Neural Networks
23.2 Explainability and Transparency in Neural Networks
24. Future Trends in Neural Networks and Machine Learning
24.1 Quantum Neural Networks: The Fusion of Quantum Computing and Neural Networks
24.2 Neural Architecture Search: The Quest for Optimal Network Design
25. Industry Applications: Where Neural Networks Meet Real-World Challenges
25.1. Healthcare: Transforming Diagnosis, Treatment, and Monitoring Through Neural Networks
25.2. Finance: Revolutionizing Risk Management, Trading, and Customer Experience with Neural Networks
25.3. Autonomous Vehicles: Navigating the Future with Neural Networks
26. Appendices
26.1. Glossary of Terms
26.2. Frequently Asked Questions
26.3. Useful Libraries and Tools
26.4. Additional Resources and Readings
26.5. About the author