Abstract: 

Learn the basics of building a PyTorch model using a structured, incremental and from first principles approach. Find out why PyTorch is the fastest growing Deep Learning framework and how to make use of its capabilities: autograd, dynamic computation graph, model classes, data loaders and more.
The main goal of this session is to show you how PyTorch works: we will start with a simple and familiar example in Numpy and ""torch"" it! At the end of it, you should be able to understand PyTorch's key components and how to assemble them together into a working model.
We will use Google Colab and work our way together into building a complete model in PyTorch. You should be comfortable using Jupyter notebooks, Numpy and, preferably, object oriented programming.

Bio: 

Daniel is a data scientist, teacher, and author of ""Deep Learning with PyTorch Step-by-Step: A Beginner's Guide"".
He has been teaching machine learning and distributed computing technologies at Data Science Retreat, the longest-running Berlin-based bootcamp, since 2016, helping more than 150 students advance their careers. Daniel is also the main contributor of two Python packages: HandySpark and DeepReplay.
His professional background includes 20 years of experience working for companies in several industries: banking, government, fintech, retail, and mobility.