Free online course Deep Learning With PyTorch

About the free online course

Mastering deep learning becomes much easier when you can see every step, run the code yourself, and understand what is happening under the hood. This free online course helps you build that foundation with PyTorch, one of the most widely used frameworks in AI research and production. You will move from core concepts to practical model building, gaining the confidence to turn datasets into working neural networks.

You start by getting comfortable with PyTorch tensors, the building blocks of everything you will train. Instead of treating them like a black box, you learn how data types behave, how reshaping and slicing affect memory, and how tensor math maps to model computations. Along the way, you practice writing clean operations and recognizing patterns such as in-place updates, which can be powerful but also lead to subtle bugs if misunderstood.

With the essentials in place, you will create and train a basic neural network end to end: loading data, defining a forward pass, selecting appropriate tensor types for features and labels, and improving your workflow for faster experimentation using cloud notebooks. You also learn how to properly evaluate models using the right tools to prevent unnecessary gradient tracking, so test metrics reflect real performance rather than training behavior.

The course then expands into convolutional neural networks, the standard approach for image tasks. You build intuition for kernels and filters, understand why convolutional layers are well-suited to vision, and see how pooling reduces spatial dimensions while preserving useful patterns. Using MNIST as a practical benchmark, you will prepare image tensors correctly, train and test a CNN, interpret results, and send brand-new images through the network to obtain predictions.

Finally, you will learn essential production-ready habits: saving and loading models correctly, running inference safely, and structuring a workflow you can reuse for future projects. By the end, you will have a clear path from raw data to trained models, and a practical set of PyTorch skills you can apply to classification problems in machine learning and computer vision.