Tutorials - Sebastian Raschka: The Fundamentals of Modern Deep Learning with PyTorch

This tutorial is aimed at Python programmers new to PyTorch and deep learning. However, even more experienced deep learning practitioners and PyTorch users may be exposed to new concepts and ideas when exploring other open source libraries to extend PyTorch. Throughout this 3.5-hour tutorial session, attendees will learn how to use PyTorch to train neural networks for image and text classification. We will discuss the individual strengths and weaknesses of deep learning and contrast it with traditional machine learning. We will be going over the PyTorch library in detail, exploring it as a tensor library, automatic differentiation library, and library for implementing deep neural networks so that you get a solid grasp of how PyTorch is structured. After getting a firm grasp of the PyTorch API, we will introduce additional open source libraries, such as PyTorch Lightning, to familiarize attendees with the modern open source stack for deep learning to take advantage of mixed-precision techniques and multi-GPU training. Note that all model code in this tutorial can be run on a laptop computer, but attendees will also be introduced to using free GPU options for this tutorial via Google Colab and Lightning to get the full benefits of the GPU training sections. The tutorial materials and additional information will be uploaded in advance on GitHub at https://github.com/rasbt/pycon2024, which also contains a Discussion Forum for questions before the event. Slides: https://pycon-assets.s3.amazonaws.com/2024/media/presentation_slides/32/2024-05-07T14%3A20%3A47.694524/all-slides.zip