Explain the concept of optimization algorithms in neural networks and compare different optimization algorithms such as stochastic gradient descent and Adam.

In neural networks, optimization algorithms play a vital role in training the model by minimizing the loss function and finding the optimal set of weights and biases. These algorithms determine how the network adjusts its parameters during the learning process. Two popular optimization algorithms used in neural networks are Stochastic Gradient Descent (SGD) and Adam (Adaptive Moment Estimation). Let's explore these algorithms and compare their characteristics: 1. Stochastic Gradient Descent (SGD): SGD is a widely used optimization algorithm in neural networks. It aims to find the optimal parameters by iteratively updating the weights based on the gradients of the loss function with respect to the parameters. The key features of SGD include: a. Batch Update: In SGD, the weight updates are performed based on small subsets or mini-batches of training data rather than the entire dataset. This speeds up the training process and helps avoid getting stuck in local minima. b. Learning Rate: SGD uses a learning rate hyperparameter that controls the step size of the weight updates. It determines how much the weights change in response to the calculated gradients. Choosing an appropriate learning rate is crucial, as a too high value can cause instabilit....