How to learn PyTorch? (3 easy steps) | 2021
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In this video, I'll give you 3 easy steps you should follow to quickly get started with PyTorch - the most popular deep learning framework in the research community.

You'll learn about:
✔️ How to quickly learn PyTorch

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✅ official tutorial: https://pytorch.org/tutorials/
✅ blog: https://towardsdatascience.com/understanding-pytorch-with-an-example-a-step-by-step-tutorial-81fc5f8c4e8e
✅ GitHub: https://github.com/gordicaleksa/pytorch-deepdream/blob/master/playground.py

Also make sure to code your own (toy) project in PyTorch, from scratch!
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⌚️ Timetable: 

0:00 What is PyTorch?
0:47 60 min Blitz tutorial + Tensorboard
2:27 step by step tutorial (blog)
3:10 DeepDream playground + code your own project
4:25 Do you know of a better way?

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#pytorch #deeplearning #framework
 In this video I'm going to show you how to quickly learn PyTorch in three easy steps. PyTorch is a deep learning framework developed by Facebook AI research and deep learning frameworks abstract away from you certain implementation details like how to construct a convolutional layer or a linear layer or how to do back propagation. It ends up with you calling a single line like loss.backward and that's it. It calculates the gradients automatically for you. So it's super popular in the research community. Most of the papers published on the biggest conferences out there for deep learning, for computer vision, NLP, etc. are using PyTorch. And allegedly it can also make your eyesight better and your skin clearer. So how can you learn PyTorch? Step number one. The official PyTorch tutorials are actually pretty decent. You should start by going through this deep learning with PyTorch, a 60 minute blitz. It's going to teach you the basics of the framework. So this chapter, What is PyTorch? It's going to teach you how to instantiate tensors, how to create like tensors of zeros, ones, random numbers, how to convert back and forth between NumPy. But it's actually the syntax. It's really similar to NumPy. That's why one of the reasons why PyTorch is so popular. So secondly, it's going to teach you some of the basics behind what happens when you call backward, i.e. when you do backpropagation. It's going to teach you how to construct a really basic neural network and define the loss and everything. And finally, training a classifier is going to teach you how to do an end to end, a small deep learning pipeline, and that's super useful. So after you're done with this 60 minute blitz, just feel free to skip the second two chapters here, learning PyTorch with examples and what is Torch and really, as that one goes really into depth of how to construct those yourself and have a better understanding. But you won't need to start developing your own neural networks using PyTorch. So just feel free to skip it for now. Visualizing models, data and training with TensorBoard might be useful as TensorBoard is a super useful tool, especially when you start training your models. It will help you log your loss, log your metrics, even your imagery, or depending what your task is about. Step number two. The second thing I'd recommend, and this is how I learned PyTorch, is go through this blog called Understanding PyTorch with an example, a step by step tutorial. And it's super structured. You have it all in one place. Just go end to end and you'll learn a bunch of stuff like about data set class, data loaders, how to create your own training loop, how to build your own simple network, loss and everything in a single place. Really nice visualizations, really nice writing. Super recommended. It's a bit longer, but you'll finish it in a couple of hours. And you're pretty much ready to start doing your own projects in PyTorch. And the third step, just start coding in PyTorch. So I have a couple of PyTorch projects on my GitHub. And you can take a look at this Deep Dream repo. It's a super good way to start understanding how PyTorch works. And if you go to this playground.py, you'll find this function called Understand PyTorch gradients. And it will help you better understand how the computational graph structure works behind the scenes. And also this one, Deep Dream Simple, will also kind of clarify how the whole gradient update works, which usually your optimizers do behind the scene, like Adam, SGD, whatever, is just being explicitly written down here in the code. So aside from going through that code, I super recommend you go and code your own project. So just pick some task, whatever it is, like take, for example, simple image classification, go ahead and write it down yourself from scratch. And you'll learn the syntax. You'll understand how the whole pipeline works. And that's pretty much it. So I hope you enjoyed this brief video on learning PyTorch. If you did, please subscribe and hit that bell icon to get notified when I upload a new video. Also, if you feel that there is a better way to learn PyTorch or some really good resource, just comment down in the comment section. And until next time, keep learning.
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