Free online coursePyTorch Deep Learning and Neural Networks

Course Description

Learn how to build and understand deep learning models with PyTorch in this free online course focused on neural networks and practical AI workflows. You will move from essential prerequisites to a clear mental model of the PyTorch API, making it easier to read, write, and debug deep learning code.

Get comfortable with the core building blocks behind modern deep learning, including tensors, shapes, ranks, axes, and how data flows through convolutional neural networks. You will also explore GPU acceleration concepts, including why CUDA matters for training neural networks efficiently.

Follow a hands-on path through creating and manipulating tensors, using operations like reshape, flatten, squeeze, broadcasting, and reduction. You will practice common patterns used in real projects, such as selecting predictions with argmax and preparing image data for model training.

Work with a well-known computer vision dataset and learn how datasets and dataloaders support training pipelines, including extraction, transformation, loading, and exploratory inspection of training data. Then, bring everything together by building a CNN in an object-oriented style, understanding layers, feature maps, and learnable weights, and connecting architectural choices to model behavior.

Ideal for learners aiming to start with PyTorch or strengthen their foundations in artificial intelligence and machine learning, this course emphasizes practical understanding and the skills needed to progress toward real-world deep learning projects.