Learn and practice PyTorch, a framework for developing deep learning models.

Become a Deep Learning Professional

In this module, we’ll be introduced to the fundamentals of PyTorch: autograd, model classes, datasets, data loaders, and more. We’ll develop models and understand them step-by-step. We'll understand the reasoning behind the code and how to avoid some common pitfalls and errors along the way.
By the time we finish this module, we’ll have a thorough understanding of the concepts and tools necessary to start developing and training our own models using PyTorch.

Deep Learning with PyTorch

## Challenge

Welcome to your fourth challenge. This problem has been designed for you to practice freely with it, so try to solve it on your own first. Take some time, and think about the different concepts we’ve explored in the course so far.

If you feel stuck, you can always check out the solution review provided in the next lesson.

Good luck!

## Jupyter notebook

Following is the Jupyter notebook provided to you, which contains your fourth challenge. Click on it to launch the application. 

# Challenge

Welcome to your fourth challenge. This problem has been designed for you to practice freely with it, so try to solve it on your own first. Take some time, and think about the different concepts we’ve explored in the course so far.

If you feel stuck, you can always check out the solution review provided in the next lesson.

Good luck!

# Jupyter notebook

Following is the Jupyter notebook provided to you, which contains your fourth challenge. Click on it to launch the application. 

Use what you've learned so far to perform the following exercise, which will help familiarize you with the concepts that we'll need later on.

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Appendix

Conclusion

Challenge 4 - Going Classy

Challenge