Mini-Batch Gradient Descent and DataLoader in PyTorch
Mini-batch gradient descent is a variant of gradient descent algorithm that is commonly used to train deep learning models. The idea behind this algorithm is to divide the training data into batches, which are then processed sequentially. In each iteration, we update the weights of all the training samples belonging to a particular batch together. This process is repeated with different batches until the whole training data has been processed. Compared to batch gradient descent, the main benefit of this approach is that it can reduce computation time and memory usage significantly as compared to processing all training samples in one shot.
DataLoader is a module in PyTorch that loads and preprocesses data for deep learning models. It can