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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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

First, coding in PyTorch is fun! Really, there is something about it that makes it very enjoyable to write code. Some say it is because it is very **pythonic** or maybe there is something else. Who knows? By the end of this course, I hope you feel that way too!

Second, maybe there are some unexpected benefits for your health. Check Andrej Karpathy's [tweet](https://bit.ly/2MQoYRo) about it!

But jokes aside, PyTorch is the [fastest-growing](https://bit.ly/37uZgLB) framework for developing Deep Learning models, and it has a [huge ecosystem](https://pytorch.org/ecosystem/). That is, there are many tools and libraries developed on top of PyTorch. It is the [preferred framework](https://bit.ly/2MTN0Lh) in academia already, and it is making its way into the industry.

Several companies are already [powered by PyTorch](https://bit.ly/2UFHFve). To name a few:

* **Facebook**: The company is the original developer of PyTorch, released in October 2016.

* **Tesla**: Watch Andrej Karpathy (AI Director at Tesla) discuss "how Tesla is using PyTorch to develop full self-driving capabilities for its vehicles" in [this video](https://bit.ly/2XXJkyo).
* **OpenAI**: In January 2020, OpenAI decided to standardize its Deep Learning framework on PyTorch ([source](https://openai.com/blog/openai-pytorch/)).
* **Fast.ai**: Fast.ai is a [library](https://docs.fast.ai/) built on top of PyTorch to simplify model training, and it is used in its [Practical Deep Learning for Coders](https://course.fast.ai/) course. The fast.ai library is deeply connected to PyTorch, and "you can't become really proficient at using fastai if you don't know PyTorch well, too". ([source](https://course.fast.ai/))
* **Uber**: The company is a significant contributor to PyTorch's ecosystem having developed libraries like [Pyro](http://pyro.ai/) (probabilistic programming) and [Horovod](https://github.com/horovod/horovod) (a distributed training framework).
* **Airbnb**: PyTorch sits at the core of the company's dialog assistant for customer service ([source](https://bit.ly/30CPhm5)).

This course aims to get you started with your own PyTorch projects while giving you a solid understanding of how it works.

Learn why PyTorch is the best choice for developing Deep Learning models.

Why PyTorch?

Introduction

Visualizing Gradient Descent

A Simple Regression Problem

Rethinking the Training Loop

Going Classy

A Simple Classification Problem

Conclusion

Appendix

Why PyTorch?