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!!!
To learn more about Lightning: https://lightning.ai/
The code demonstrated this video can be downloaded here:
https://lightning.ai/lightning-ai/studios/statquest-introduction-to-coding-neural-networks-with-pytorch?view=public§ion=all
This StatQuest assumes that you are already familiar with...
Neural Networks:
https://youtu.be/CqOfi41LfDw
Backpropagation:
https://youtu.be/IN2XmBhILt4
The ReLU Activation Function:
https://youtu.be/68BZ5f7P94E
Tensors:
https://youtu.be/L35fFDpwIM4
To install PyTorch see: https://pytorch.org/get-started/locally/
To install matplotlib, see: https://matplotlib.org/stable/users/getting_started/
To install seaborn, see: https://seaborn.pydata.org/installing.html
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
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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