PyTorch 101

Focused View

Andrea Giussani

1:00:41

13 View

1. Introduction.mp4

04:45

2. Tensors in PyTorch.mp4

12:44

3. Operations between Tensors.mp4

10:36

4. PyTorch Autograd Module.mp4

10:45

5. Creating a Dataset in PyTorch.mp4

11:24

6. Backpropagation in PyTorch.mp4

09:21

7. Summary.mp4

01:06

Description

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

More details

User Reviews

Rating

average 0

Total votes0

Focused display

Python

PyTorch

Andrea Giussani

Instructor's Courses

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.

Cloud Academy

View courses Cloud Academy

Join thousands of users in achieving your personal goals through Cloud Academy. Score job-ready tech skills that you can practice in a real environment, without the risk of extra costs or making mistakes. It’s simply the smartest way to gain certifications and get career-ready.