edfine/fine-finds

Papers that I like. I won't lose them here.

Fine-finds

Papers that I like. I won't lose them here.

These are a series of papers on ML, AI and Data that I find useful.

Here are some web sites that students like as well.

https://towardsdatascience.com/top-sources-for-machine-learning-datasets-bb6d0dc3378b - open source datasets

https://seeing-theory.brown.edu/ - visualization of statistics

http://www.sandia.gov/~wpk/slides/avatar-ensembles.pdf Decision trees and ensembles

https://www.mdpi.com/2079-9292/8/3/292/pdf-vor Nice recent overview of some of the state of the field

https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/ - visualization of the DBSCAN algorithm

https://jeremykun.com/main-content/ - Multiple blog posts giving background mathematical motivation to various ML and AI concepts. Examples in python.

https://github.com/scikit-learn-contrib/imbalanced-learn - A python library to offer lots of algorithms to resample inbalanced datasets.

https://colah.github.io/posts/2014-03-NN-Manifolds-Topology/ - Nice visual explanation of how the neural network nonlinearity works.

https://towardsdatascience.com/the-complete-reinforcement-learning-dictionary-e16230b7d24e - Reinforcement Learning vocabulary