Equinox is a recent addition to the environment of deep learning frameworks in Python. In this video, we will use it to train a Multilayer Perceptron to mimic the sine function, also using JAX primitives jax.vmap and jax.jit. Here is the code: github.com/Ceyron/machine-lea...
👉 This educational series is supported by the world-leaders in integrating machine learning and artificial intelligence with simulation and scientific computing, Pasteur Labs and Institute for Simulation Intelligence. Check out simulation.science/ for more on their pursuit of 'Nobel-Turing' technologies (arxiv.org/abs/2112.03235 ), and for partnership or career opportunities.
Also check out the docs of Equinox: docs.kidger.site/equinox/
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Timestamps:
00:00 Intro
01:03 Imports
01:34 Hyperparameters/Constants
02:14 Generating a toy sine dataset
04:38 Setting up MLP architecture in Equinox
09:24 Initial prediction on the dataset
11:39 Defining a loss function
12:50 What is learning? Why do we need gradients?
13:27 Function transformation with autodiff
16:08 Setting up optimizer from optax
18:58 Separate function for one optimization step
20:30 Training loop
21:28 JIT compilation of the update step function
22:45 Plotting loss history
23:17 Prediction with trained parameters
24:08 Summary
26:09 Outro