ML Book 10 - Advanced Machine Learning/ Optimization and Regularization for Dummies

Table of Contents

1. Introduction

1.1 What is Machine Learning?

1.2 Why Optimization and Regularization are Important? 1.3 Overview of the Book

2. Optimization Techniques

2.1 Gradient Descent and Its Variants

2.2 Stochastic and Mini-Batch Methods

2.3 Momentum and Adaptive Learning Rates

2.4 Constrained Optimization and Lagrange Multipliers 3. Regularization Methods

3.1 L1 and L2 Regularization

3.2 Dropout and Batch Normalization

3.3 Early Stopping and Cross-Validation

3.4 Data Augmentation and Noise Injection

4. Applications and Examples

4.1 Linear and Logistic Regression

4.2 Neural Networks and Deep Learning

4.3 Support Vector Machines and Kernel Methods

4.4 Decision Trees and Ensemble Methods

5. Conclusion

5.1 Summary of the Main Points

5.2 Further Reading and Resources