11. L1 and L2 Regularization in Neural Networks - Explained with Code!
In this video, we dive into two crucial regularization techniques for neural networks—L1 regularization (Lasso) and L2 regularization (Ridge). These methods are essential for preventing overfitting, helping neural networks generalize better on unseen data.
You'll learn:
What L1 and L2 regularization are and how they work.
How L1 encourages sparsity in model weights (leading to feature selection).
How L2 prevents large weights and promotes smaller, more balanced weights.
When to use L1 vs L2 regularization in your machine learning models.
How to implement both techniques in Python using TensorFlow/Keras.
A practical example using the Iris dataset for binary classification.
Visual comparisons of the weight distributions with matplotlib.
By the end of this video, you'll have a solid understanding of how regularization can improve the performance of your neural network models!
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