This repository contains my solutions to problems from TensorTonic, shared openly to benefit the broader machine learning community.
Each sub-directory contains a solution .py file and a concise .md document outlining the theoretical foundations.
TensorTonic is a platform where you can implement core algorithms of Machine Learning from scratch. This repository contains my personal solutions to these problems, automatically synchronized from the platform.
The solutions in this repository adhere to two guiding principles:
- Readability Variable names and code structure mirror mathematical notation wherever practical, minimising the gap between theory and implementation.
- Minimalism No unnecessary abstractions are introduced. The objective is transparent, auditable code that reflects the algorithm directly.
This repository is released under the MIT Licence. See LICENSE for full text.
Maintained by Keegan Dsouza. For questions or discussion about any solution, please open an issue on the issue tracker.
