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

What's the purpose of calling `model.eval()`?

What’s the purpose of calling <code>model.eval()</code>?

It is best practice to wrap the validation inner loop with a `torch.no_grad()` context manager:

It is best practice to wrap the validation inner loop with a <code>torch.no_grad()</code> context manager:

Check all that apply. The checkpoint of a model that’s going to be trained further should include:

Check all that apply. The checkpoint of a model that’s going to be trained further should include:

After fully training a model, you can start making predictions on unseen data, but first you need to set the model to:

After fully training a model, you can start making predictions on unseen data, but first you need to set the model to:

Test your understanding of the concepts explained in this chapter with this quiz!

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Appendix

Conclusion

Quiz