PyTorch Autograd Module
This lesson introduces you to PyTorch and focuses on two main concepts: PyTorch tensors and the autograd module. We are going to get our hands dirty throughout the lesson, using a demo environment to explore the methodologies covered. We’ll look at the pros and cons of each method, and when they should be used.
Learning Objectives
- Create a tensor in PyTorch
- Understand when to use the autograd attribute
- Create a dataset in PyTorch
- Understand what backpropagation is and why it is important
Intended Audience
This lesson is intended for anyone interested in machine learning, and especially for data scientists and data engineers.
Prerequisites
To follow along with this lesson, you should have PyTorch version 1.5 or later.
Resources
The Python scripts used in this lesson can be found in the GitHub repo here: https://github.com/cloudacademy/ca-pytorch-101
Andrea is a Data Scientist at Cloud Academy. He is passionate about statistical modeling and machine learning algorithms, especially for solving business tasks.
He holds a PhD in Statistics, and he has published in several peer-reviewed academic journals. He is also the author of the book Applied Machine Learning with Python.