Exploring Gradient Clipping & Weight Initialization in Deep Learning – Day 44

Understanding Gradient Clipping and Weight Initialization Techniques in Deep Learning In this part, we explore the fundamental techniques of gradient clipping and weight initialization in more detail. Both of these methods play a critical role in ensuring deep learning models train efficiently and avoid issues like exploding or vanishing gradients. Gradient Clipping: Controlling Exploding Gradients When training deep learning models, especially very deep or recurrent neural networks (RNNs), one of the main challenges is dealing with exploding gradients. This happens when the gradients (which are used to update the model’s weights) grow too large during backpropagation, causing unstable training or even model failure. Gradient clipping is a method used to limit the magnitude of the gradients during training. Here’s how it works and why it’s useful: How Gradient Clipping Works: During backpropagation, the gradients are calculated for each parameter. If a gradient exceeds a predefined threshold, it is scaled down to fit within that threshold. There are two main types of gradient clipping: Norm-based clipping: The magnitude (norm) of the entire gradient vector is computed. If the norm exceeds the threshold, the gradients are scaled down proportionally. Value-based clipping: If any individual gradient component exceeds a set value, that specific…