PyTorch 101: Building a Model Step-by-Step by Daniel Voigt Godoy

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 training 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.

Learning Objectives

• Understand the basic building blocks of PyTorch: tensors, autograd, models, optimizers, losses, datasets, and data loaders.
• Identify the basic steps of gradient descent, and how to use PyTorch to make each one of them more automatic.
• Build, train, and evaluate a model using mini-batch gradient descent.

Website: https://hubs.li/Q01hb8Rk0