ML-Optimizers-JAX
yaglm
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ML-Optimizers-JAX | yaglm | |
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1 | 1 | |
40 | 49 | |
- | - | |
4.5 | 0.0 | |
almost 3 years ago | about 1 year ago | |
Python | Python | |
- | MIT License |
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ML-Optimizers-JAX
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ML Optimizers from scratch using JAX
Github link (includes a link to a Kaggle notebook to run it directly) - shreyansh26/ML-Optimizers-JAX
yaglm
What are some alternatives?
RAdam - On the Variance of the Adaptive Learning Rate and Beyond
sweetviz - Visualize and compare datasets, target values and associations, with one line of code.
DemonRangerOptimizer - Quasi Hyperbolic Rectified DEMON Adam/Amsgrad with AdaMod, Gradient Centralization, Lookahead, iterative averaging and decorrelated Weight Decay
hal9001 - 🤠📿 The Highly Adaptive Lasso
dm-haiku - JAX-based neural network library
scikit-learn - scikit-learn: machine learning in Python
trax - Trax — Deep Learning with Clear Code and Speed
AdasOptimizer - ADAS is short for Adaptive Step Size, it's an optimizer that unlike other optimizers that just normalize the derivative, it fine-tunes the step size, truly making step size scheduling obsolete, achieving state-of-the-art training performance
dnn_from_scratch - A high level deep learning library for Convolutional Neural Networks,GANs and more, made from scratch(numpy/cupy implementation).
flaxOptimizers - A collection of optimizers, some arcane others well known, for Flax.