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Watch me code a Neural Network from Scratch! 🥳 In this 3rd video of the JAX tutorials series.
In this video, I create an MLP (multi-layer perceptron) and train it as a classifier on MNIST (although it's trivial to use a more complex dataset) - all this in pure JAX (no Flax/Haiku/Optax).
I then add cool visualizations such as:
* Visualizing MLP's learned weights
* Visualizing embeddings of a batch of images in t-SNE
* Finally, we analyze the dead neurons
Credit:
Got the inspiration from the official advanced JAX tutorial here: [ Ссылка ]
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✅ Get started with JAX GitHub: [ Ссылка ]
✅ Dead neuron article: [ Ссылка ]
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⌚️ Timetable:
00:00:00 Intro, structuring the code
00:03:10 MLP initialization function
00:13:30 Prediction function
00:24:10 PyTorch MNIST dataset
00:31:40 PyTorch data loaders
00:39:55 Training loop
00:49:15 Adding the accuracy metric
01:01:45 Visualize the image and prediction
01:04:40 Small code refactoring
01:09:25 Visualizing MLP weights
01:11:30 Visualizing embeddings using t-SNE
01:17:55 Analyzing dead neurons
01:24:35 Outro
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Huge thank you to these AI Epiphany patreons:
Eli Mahler
Petar Veličković
Bartłomiej Danek
Zvonimir Sabljic
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#jax #neuralnetwork #coding
