Dive into a comprehensive 23-minute video tutorial on PyTorch, one of the most popular tools for creating Neural Networks. Walk through a simple example, step-by-step, to learn how to create a new neural network from scratch, make predictions, graph the output, and optimize parameters using backpropagation. Gain hands-on experience with coding preliminaries, building a neural network in PyTorch, visualizing its output, and implementing backpropagation for parameter optimization. Ideal for those already familiar with Neural Networks, Backpropagation, ReLU Activation Function, and Tensors, this tutorial provides practical insights into PyTorch's functionality and application in machine learning projects.
Overview
Syllabus
Awesome song and introduction
Coding preliminaries
Creating a neural network in PyTorch
Graphing the neural network's output
Optimizing a parameter with backpropagation
Taught by
StatQuest with Josh Starmer
