PyTorch Functions

Recently, I've completed 30 Days of ML with Pytorch, where I explored all the major machine learning algorithms and basics of deep learning with concepts like activation, optimizer, loss functions etc. However, the idea behind starting 30 Days of ML with PyTorch is to learn the implementation of machine learning algorithm along with strengthening my grasp on PyTorch Library. So moving forward with this blog, I will share few important functions in PyTorch which remains part of every ML and DL algorithms. In neural networks, how we initialize our weights plays a great role in convergence of the model and a common but inefficient way to initialize weight is to declare weights as zeros, but other efficient way includes using uniform or normal or xaviers technique to initialize weights. It is applied for creating dropout functionality in neural network for regularization.