Momentum – part 3 – day 35

Understanding Gradient Descent and Momentum in Deep Learning Comprehensive Guide: Understanding Gradient Descent and Momentum in Deep Learning Gradient descent is a cornerstone algorithm in the field of deep learning, serving as the primary method by which neural networks optimize their weights to minimize the loss function. This article will delve into the principles of gradient descent, its importance in deep learning, how momentum enhances its performance, and the role it plays in model training. We will also explore practical examples to illustrate these concepts. What is Gradient Descent? Gradient Descent is an optimization algorithm used to minimize a loss function by iteratively adjusting the model’s parameters (weights and biases). The loss function measures the discrepancy between the model’s predictions and the actual target values. The goal of gradient descent is to find the set of parameters that minimize this loss function, thereby improving the model’s accuracy. The Gradient Descent Formula The basic update rule for gradient descent is expressed as: Where: represents the model parameters at iteration . is the learning rate, a hyperparameter that determines the step size for each iteration. is the gradient of the loss function with respect to the parameters at the previous iteration. How…