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https://github.com/mytechnotalent/renn
Inspired by Andrej Karpathy’s micrograd, this lecture builds a neural network from scratch, manually deriving gradients, automating backpropagation, and leveraging the TANH activation for nonlinearity. We bridge to PyTorch, demonstrating gradient descent’s power to minimize loss and reveal neural network fundamentals.
https://github.com/mytechnotalent/renn
ai artificial-intelligence artificial-neural-networks machine-learning neural-network neural-networks neural-networks-from-scratch nn nnfs pytorch reverse-engineering stem
Last synced: about 2 months ago
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Inspired by Andrej Karpathy’s micrograd, this lecture builds a neural network from scratch, manually deriving gradients, automating backpropagation, and leveraging the TANH activation for nonlinearity. We bridge to PyTorch, demonstrating gradient descent’s power to minimize loss and reveal neural network fundamentals.
- Host: GitHub
- URL: https://github.com/mytechnotalent/renn
- Owner: mytechnotalent
- License: apache-2.0
- Created: 2024-11-22T00:43:22.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-22T00:52:47.000Z (2 months ago)
- Last Synced: 2024-12-02T16:25:47.017Z (2 months ago)
- Topics: ai, artificial-intelligence, artificial-neural-networks, machine-learning, neural-network, neural-networks, neural-networks-from-scratch, nn, nnfs, pytorch, reverse-engineering, stem
- Language: Jupyter Notebook
- Homepage:
- Size: 1.32 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0