This PyTorch 101 Crash Course for Beginners (2026) by Daniel Bourke is designed to give you a fast but solid introduction to deep learning using PyTorch. It is perfect for complete beginners who want to understand how modern AI models are built and trained.

The course starts with the fundamentals of PyTorch, including tensors, which are the core data structure used for all computations. You will learn how to create, manipulate, and perform operations on tensors, which is essential for building neural networks.

Next, the course introduces automatic differentiation and how PyTorch handles gradients using Autograd. This is a key concept that allows models to learn from data. You will then move into building your first neural network, understanding how layers, weights, and activation functions work together.

The training process is explained step by step, including forward pass, loss calculation, and backpropagation. You will also learn how to write a training loop and improve model performance using optimization techniques.

By the end of this crash course, you will have a clear understanding of how PyTorch works and how to use it to build simple deep learning models