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 to expect from this chapter

In this chapter, we will:

* Build a function to perform training steps.
* Implement our own dataset class.
* Use **data loaders** to generate mini-batches.
* Build a function to perform mini-batch gradient descent.
* Evaluate our model.
* Integrate **TensorBoard** to monitor model training.
* **Save/checkpoint** our model to disk.
* Load our model from disk to resume training or to deploy.

# What to expect from this chapter

In this chapter, we will:

* Build a function to perform training steps.
* Implement our own dataset class.
* Use **data loaders** to generate mini-batches.
* Build a function to perform mini-batch gradient descent.
* Evaluate our model.
* Integrate **TensorBoard** to monitor model training.
* **Save/checkpoint** our model to disk.
* Load our model from disk to resume training or to deploy.

Learn about the learning outcomes of this chapter. 

Spoilers

Learn about the learning outcomes of this chapter.

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Appendix

Conclusion

Spoilers

What to expect from this chapter