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L1 norm weight penalty
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Total time needed:
~2 hours
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Learning Objectives
Mathematical foundations behind Deep Learning
Potential Use Cases
Mathematical foundations behind Deep Learning
Target Audience
INTERMEDIATE
People interested in knowing how Deep Learning model training works.
Go through the following
annotated items
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ARTICLE
1. Conceptual understanding of L1 norm weight penalty
Gives a visual understanding of what L1 penalty does for a simple 2-d case.
20 minutes
ARTICLE
2. Practical implementation of L1 penalty
How is L1 regularization implementation different from its conceptual understanding?
20 minutes
BOOK_CHAPTER
3. Mathematical understanding of what L1 penalty does to weights learnt as compared to weights of unregularized cost function.
Mathematical understanding of what L1 penalty does to weights
30 minutes
ARTICLE
4. Difference between restrictions imposed by L1 and L2 penalties on weights
Difference between restrictions imposed by L1 and L2 penalties on weights
15 minutes
BOOK_CHAPTER
5. What is regularization for neural networks?
What is regularization?
15 minutes
BOOK_CHAPTER
6. How does penalizing parameters lead to regularization?
Why is penalty applied only to weights and not biases
10 minutes
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