Gradient Descent in PyTorch with Autograd - Lab

Follow along with a hands-on lab tutorial exploring PyTorch's Autograd library implementation of derivatives and gradient descent concepts. Dive into practical demonstrations using a provided Colab notebook to understand autograd functionality in PyTorch, explore derivative calculations, and visualize computation graphs using torchviz. Learn to implement backward passes for perceptrons, apply gradient descent techniques, and work with MNIST model computation graphs. Master cross-entropy loss implementation in PyTorch and discover bonus content on visualizing forward passes using torchview. Access comprehensive code examples and step-by-step guidance through each concept, from basic autograd principles to advanced neural network implementations.