This small respository is just to save some of the notes I did throught a journey I commited to apply machine learning to different datasets, so some notebooks are not well organized and I used multiple resources to learn some topics. Most of the datasets can be found in kaggle. The repository is more of a brainstorm. In short has the next contents
- Optimization algorithms: A small list of some algorithms I decided to write from scratch (i.e., SGD and ADAM), implemented as in
Kochenderfer, M. J., & Wheeler, T. A. (2019). Algorithms for optimization. Mit Press.
- Machine Learning Applications: Some examples of the familiy of algorithms of ML applied in kaggle datasets, inspired from what I readed in
Alpaydin, E. (2020). Introduction to machine learning. MIT press.
- Mathematic fundamentals: Principles of math used in machine learning inspired by the book
Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press.
I hope this could be useful for someone interested in start learning ML, so that he/she could see a broad (and in this case a bit mess) view of the field.