PyTorch Crash Course - Getting Started with Deep Learning

Learn how to get started with PyTorch in this Crash Course. It teaches you all important concepts about this Deep Learning framework.

Resources:

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#MachineLearning #DeepLearning

PyTorch Getting Started
PyTorch Installation
PyTorch Neural Net
PyTorch CNN
PyTorch Dataset
PyTorch DataLoader
PyTorch Optimization

Timeline:
00:00 Intro & Overview
00:54 Installation & Overview
02:37 Tensor Basics
11:14 Autograd
17:41 Linear Regression Autograd
20:59 Model, Loss & Optimizer
27:11 Neural Network
38:08 Convolutional Neural Net

[shy]

I highly appreciate that you didnt pollute the video with much deep learning concepts. The Main focus should be "you know deep learning you are familiar with the concepts and maybe another framework but you want to gettting started with pytorch and here is what should you know"
Thank you!

[ckb]

Best tutorial I have ever gone through. To the point, No fluff! Congrats on building such a neat video!

[mobasshirbhuiyanshagor]

This video is recommended to all who starting with Pytorch. With my 5 years of experience in this field, I can assure you that this video will sharpen your understanding in a great way.

[tolulopeoyemakinde]

This is the best crash course i have seen online, I was able to write my own model for signal processing

[flakky]

This 50 minute video is better/produtive than a whole 24 hour video...if you know you know

[mutalasuragemohammed]

This is a 6 months course, in one package. Thank you.

[moritzr]

great tutorial, i took me around 2hrs to complete while asking chatgpt for help throughout but now i understand it all quite well. thanks a lot

[volodymyrtruba]

I am working in DL sphere for 6 years, this is golden tutorial! Well Done!

[ElNachoMacho]

In step 4. Frist Neural Net, the code breaks in the line "example_data, example_targets = examples.next()", it throws an attribute error because instead of examples.next() it now should be next(examples)

[zeldaoot]

This is a great, quick tutorial for someone with some experience in python and in other deep learning frameworks like Keras but looking to expand into PyTorch. You don't waste any time! I found myself frequently pausing the video while following along, so it took a good 5 hours for me to get through this 50-minute video. It was time well spent, though.

The learning curve may be a little steep for someone just starting out with deep learning, but then such people usually won't be using PyTorch right away.

[sanchosanjar]

This is art! Short, sharp and to the point!

[aymericobled]

A clear, precise, concise tutorial, superb work thank you very much

[muhammadabubakarsaddique]

Awesome!!! Highly recommended!!
I usually work with TF most of the time. But due to some research work i have to learn PyTorch!!
This tutorial is like getting Big Picture idea of coding with PyTorch!!

Bravo!!

[samiatbola-matanmi]

I don't comment on videos but for this I have to. This is the definition of a crash course, everything needed to know is contained. Thanks so much this has really given me confidence in pytorch.

[illtemperedklavier-irfy]

This is an excellent video, it told me what I wanted to know and needed to know, efficiently. It was so condensed that I probably spent about 5 hours on it, because I wanted to run it on my computer, and see some of the partial outputs and play around, but now I feel like I get how Pytorch flows work, because I have not found Pytorch as intuitive as Tensorflow, though there are a lot of really great things about how it works (I learned programming from people who did it old school). Thank you very much for making this!

[terryliu]

Awesome video! Best tutorial on PyTorch!

[RKDivineLove]

Thank you for sharing this video. The explanation was fantastic and incredibly helpful!🙌

[koshkakk]

At 20:20 don't make the mistake I did of writing w = w - learning_rate * w.grad as it basically creates a new w and messes autograd stuff up ( sorry if I'm using the wrong terminology ). To ensure it's 'inline' you can also write w.sub_(learning_rate * w.grad)

[anoushkagade]

This was a wonderful crash course for new beginners like me! Thank you!

[adityakamath]

Thank you so much for this tutorial!!!