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A collection of foundational implementations built from scratch — including ML algorithms, neural networks, and data science utilities — without high-level libraries.

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INTRODUCTION

This repository will contain from scratch implementations of various algorithms and techniques in the domains of Machine Learning, Neural Networks, Quantitative Finance, and more. The focus will be on understanding the underlying principles and mechanics of these algorithms, rather than relying on high-level libraries.

More domains and algorithms will be added over time, so feel free to contribute or suggest new topics.

CONTRIBUTING

If you would like to contribute, please fork the repository and submit a pull request. Contributions are welcome, whether they are new algorithms, improvements to existing implementations, or documentation enhancements.

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A collection of foundational implementations built from scratch — including ML algorithms, neural networks, and data science utilities — without high-level libraries.

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