ReLU efficiently counters vanishing gradients but not exploding during training. In machine learning, we use feed-forward neural networks that rely on matrix multiplications at each step. we can face issues like vanishing gradients and exploding gradients. Vanishing gradients mean that the weights shrink towards zero, causing the model to forget or useless neurons. Exploding gradients mean the weights grow too large, leading to massive errors. To fix this, we use activation functions. Three popu

Talk: The implicit bias of optimization algorithms in deep learning