The StatQuest Introduction to PyTorch

PyTorch is one of the most popular tools for making Neural Networks. This StatQuest walks you through a simple example of how to use PyTorch one step at a time. By the end of this StatQuest, you'll know how to create a new neural network from scratch, make predictions and graph the output, and optimize a parameter using backpropagation. BAM!!!

The code demonstrated this video can be downloaded here:

This StatQuest assumes that you are already familiar with...

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0:00 Awesome song and introduction
1:38 Coding preliminaries
2:15 Creating a neural network in PyTorch
7:54 Graphing the neural network's output
10:47 Optimizing a parameter with backpropagation

#StatQuest #NeuralNetworks #PyTorch

[statquest]

This StatQuest assumes that you are already familiar with...

[santoshmohanram]

Favorite teacher with my favorite Deep learning framework. Lucky to have you. Thanks brother🙏

[firesongs]

Please continue to go through every single line of code including the parameters with excruciating detail like you do.

None of my professors went over each line like that cuz they always "assumed we already knew" and everyone in the class who didnt already know was afraid to ask to avoid looking stupid. Thank you.

[insushin]

StatQuest is the GOAT in statistics, machine learning, and deep learning! You're videos are really helping me understanding the concepts and outline of these fields! Love from Korea!

[youlahr]

Ive used PyTorch for projects before, but I can honestly say that I never fully understood the workings of building a model. I knew that i needed the peices you mentioned, but not why I needed them. You've just explained it incredibly. Please don't stop making this series!!

[gummybear]

What a blessing this is. You are indeed the Richard Feynman of Data Science.

[karlnikolasalcala]

YOU ARE THE BEST TEACHER EVER JOSHH!! I wish you can feel the raw feeling we feel when we watch your videos

[footballistaedit]

Thanks for the best content you bring. I hope you continue to make a full pytorch playlist

[jonnhw]

Was looking for a pytorch resource and was disappointed when this channel didnt have one yet but then this got uploaded. Really a blessing to the people haha

[viveksundaram]

Man, you are love. I started my neural net journey from your videos and it's the best decision I made. Thank you

[jamilahmed]

I have lived long enough to watch videos and understand nothing about ML stuffs, until I saw your videos. I truly wish your well being <3

[binhle]

AMAZING video. This is exactly what beginners need to start the Pytorch journey with a semi solid footing instead of mindless copying.
Yoy must have spent so much time for your AWESOME videos.

GREATLY appreciate your effort. Keep up the good work.

[AlbertsJohann]

What a great feeling when it all clicks after learning about all these concepts in isolation. All thanks to an incredibly brilliant teacher! Triple BAM!!!

[kaanzt]

That's really cool explanation! Please continue this PyTorch series, we really need it. BAM!

[ToyExamples]

The style of storytelling is just so unique and friendly

[sapnasharma]

Thanks for the awesome tutorial! You make the most difficult things so easy to understand, specially with the visuals and the arrows and all! The comments written on the right hand side make it so more helpful to pause and absorb. I would never miss a video of your tutorials!

[Nonexistent_]

Thank you sir. You have no idea how valuable and helpful your videos are. Keep this good work running

[MugIce-lrui]

Hello! Not sure if anyone's pointed this out yet, but the code on 10:14, 12:09, and 22:42 needs a small addition, `plt.show()`, or else it won't show the graph. Though, maybe 2 years ago when this video was made you didn't need that, I'm not sure, haha.

Other than that, this is an awesome tutorial that quite literally takes anyone through the process step-by-step, and even tells you some neat fun facts (like the sns nickname) and explanations like how `loss.backward()` works.
TRIPLE BAM indeed! Thanks for the awesome tutorials and videos you put out 👍

[frederikschnebel]

Thanks so much for this gem John! Literally got a PyTorch project coming up and your timing is just perfect. Greatly appreciate the content, keep up the good work :)

[ais]

BIG LIKE before watching 👍🏻 please continue the pytorch series