This repository contains personal experiments, as well as notes and references, in relation to my study of classical machine learning.
Special notes:
- This repository would be frequently updated as new experiments/notes/papers come up.
- It is not recommended for the implementations to be a definitive resource or to be copied as is as some are deemed to have errors or may be incorrect (should you wish though to communicate about the implementations, feel free to reach out at agquional@up.edu.ph). These implementations are uploaded here anyway to keep track of my personal learning.
AI221- contains exercises submitted as part of the Classical Machine Learning (AI 221) course in the AI Program of UP Diliman; also contains re-implementations of some of Dr. Pilario's notebooks (repository here)
- The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos
- "Periodic table of machine learning" could fuel AI discovery [MIT News]
- I-Con: A Unifying Framework for Representation Learning (arXiv paper | website)
- Deep Learning is Not So Mysterious or Different [arXiv]
- NoProp: Training Neural Networks without Back-propagation or Forward-propagation [arXiv]
- Neural Networks Explained (via Ultralytics)
- TensorFlow Neural Network Playground
- I Won $10,000 in a Machine Learning Competition — Here’s My Complete Strategy