Initialization in Deep Learning
When training a neural network, after defining the model architecture, a crucial step is to properly initialize the weights. This initialization is essential to achieve stable and efficient training. Proper weight initialization helps prevent issues such as exploding or vanishing gradients, which can significantly hinder the learning process. It turns out that if you do it wrong, it can lead to exploding or vanishing weights and gradients. That means that either the weights of the model explode to infinity, or they vanish to 0 (literally, because computers can’t represent infinitely accurate floating point numbers), which make training deep neural networks very challenging. ...