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

The `train-test` split should happen BEFORE any kind of preprocessing.

The <code>train-test</code> split should happen BEFORE any kind of preprocessing.

If you have 80% chance of winning a bet, the odds ratio is:

If you have 80% chance of winning a bet, the odds ratio is:

If you have 20% chance of winning a bet, the odds ratio is:

If you have 20% chance of winning a bet, the odds ratio is:

A logit value of zero corresponds to a probability, which equals:

A logit value of zero corresponds to a probability, which equals:

We can convert logits into probabilities using the:

We can convert logits into probabilities using the:

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