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Why do we often refer to L2 regularization as weight decay?
This term is the reason why L2 regularization is often referred to as weight decay since it makes the weights smaller. Hence you can see why regularization works, it makes the weights of the network smaller.
How does L2 regularization work?
L2 regularization forces weights toward zero but it does not make them exactly zero. L2 regularization acts like a force that removes a small percentage of weights at each iteration. Therefore, weights will never be equal to zero.
Why do we use L1 and L2 regularization?
The two main reasons that cause a model to be complex are: Total number of features (handled by L1 regularization), or The weights of features (handled by L2 regularization) It is also called regularization for sparsity. As the name suggests, it is used to handle sparse vectors which consist of mostly zeroes.
Why is the penalty term small in L2 regularization?
We would prefer to take smaller θ’s, or θ’s that are close to zero to drive the penalty term small. It is also called L2 regularization and it pushes weight with force vectors perpendicular to the surface of a sphere, so they’re likely to be pretty similar, since most of the volume of the sphere lies in areas where weights are similar.
What’s the difference between the L1 and L2?
Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights. As follows: L1 regularization on least squares:
When to use L1 regularization in feature vector space?
L1 Regularization It is also called regularization for sparsity. As the name suggests, it is used to handle sparse vectors which consist of mostly zeroes. Sparse vectors typically result in very high-dimensional feature vector space.