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The Dual Paths of Regularization
Mastering L1 and L2 in Machine Learning
Premium AI Book (PDF/ePub) - 200+ pages
Table of Contents
1. Unpacking Regularization- The Foundation of Machine Learning
- Understanding Regularization
- The Role of Regularization in Overfitting Prevention
2. Journey into L1 Regularization
- L1 Regularization: The Concept and Math
- Practical Implementations of L1
- Advantages and Limitations of L1
3. Exploring L2 Regularization
- Deciphering L2: Theory and Application
- The L2 Approach in Various Algorithms
- Benefits and Drawbacks of L2
4. Regularization in Linear Models
- Simplifying Complexity with L1 & L2
- Regularization Paths in Linear Regression
- Comparative Analysis: L1 vs L2 in Linearity
5. Delving into Non-Linear Complexities
- Regularization in Non-Linear Models
- The Impact of L1 & L2 on Overfitting
- Case Studies: Regularization in Action
6. Regularization Techniques and Hyperparameters
- Tuning for Optimal Performance
- Cross-Validation and Regularization
- Hyperparameter Optimization: A Systematic Approach
7. Sparse Solutions with L1 Regularization
- Promoting Sparsity in Models
- Feature Selection via L1 Regularization
- Sparse Models in the Real World
8. The Geometry of L2 Regularization
- Visualizing L2 in Multidimensional Space
- The Spherical Constraints of L2
- Optimization and the Geometry of L2
9. Mixed Regularization Techniques
- Combining L1 and L2: The Elastic Net
- Balancing Bias and Variance
- Hybrid Approaches: Best of Both Worlds
10. Regularization in Deep Learning
- Deep Networks and Overfitting
- Applying L1 and L2 in Neural Networks
- Advanced Regularization Techniques in Deep Learning
11. Evaluating Models with Regularization
- Performance Metrics and Regularization
- Model Complexity and Evaluation
- Interpreting Results: A Holistic View
12. Future Trends in Regularization
- Emerging Research and Developments
- Regularization in Evolving Algorithms
- The Horizon of Regularization Techniques
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