shreyansh26/ML-Optimizers-JAX
Toy implementations of some popular ML optimizers using Python/JAX
This project helps machine learning practitioners understand the inner workings of various optimization algorithms. It takes a dataset with numerical features and a linear regression model, then applies different optimizers to train the model. The output shows how each optimizer adjusts the model's parameters to minimize error, providing a clear illustration of their behavior.
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Use this if you are a machine learning student or researcher who wants to deepen your understanding of how different gradient-based optimizers work by seeing them implemented from scratch on a simple model.
Not ideal if you are looking for a production-ready library to train complex machine learning models efficiently, as this is a toy implementation for educational purposes.
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Python
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Last pushed
Jun 20, 2021
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