Delve into PyTorch basics: autograd, model classes, datasets, and data loaders. Gain insights into model development while avoiding common pitfalls. Start creating and training your own PyTorch models.

Deep learning with Pytorch, A beginner's Guide By Daniel Godoy.png

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jupyterp-classy

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This course is designed to provide you with an easy-to-follow, structured, incremental, and from-first-principles approach to learning PyTorch. In this course, you’ll be introduced to the fundamentals of PyTorch: autograd, model classes, datasets, data loaders, and more. You will develop, step-by-step, not only the models themselves but also your understanding of them. You'll be shown both the reasoning behind the code and how to avoid some common pitfalls and errors along the way. By the time you finish this course, you’ll have a thorough understanding of the concepts and tools necessary to start developing and training your own models using PyTorch.

Deep Learning with PyTorch Step-by-Step: Part I - Fundamentals

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!

Quiz

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Quiz