PyTorch is currently one of the most popular deep learning frameworks. It is an open-source library built upon the Torch Library.
Most tutorials assume you're comfortable jumping straight into code. I made a visual introduction that walks through the core concepts step by step, with animations and diagrams instead of walls of text.
Whether you're completely new to deep learning or just want a clearer mental model of what's happening under the hood, this should help.
What's covered:
Tensors - what they are and how PyTorch thinks about data
Initialisation Functions - how weights get set up before training
The basic ML training loop - forward pass, loss, backward, update
Activation functions - ReLU, Sigmoid, Tanh and when in the training pipeline they are used
...and a few more concepts to tie it all together!
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