useful_resources.md

Cheatsheets

• Machine Learning cheatsheets for Stanford's CS 229 (Afshine Amidi and Shervine Amidi )

  https://github.com/afshinea/stanford-cs-229-machine-learning

• Probability (wzchen)

  https://github.com/wzchen/probability_cheatsheet/raw/master/probability_cheatsheet.pdf

• Jupyter (Datacamp)

  https://datacamp-community-prod.s3.amazonaws.com/48093c40-5303-45f4-bbf9-0c96c0133c40

• Numpy (Datacamp)

  https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Numpy_Python_Cheat_Sheet.pdf

• Scikit-learn (Datacamp) https://datacamp-community-prod.s3.amazonaws.com/5433fa18-9f43-44cc-b228-74672efcd116

• Scikit-learn

• Matplotlib (Datacamp)

  https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Matplotlib_Cheat_Sheet.pdf

• Seaborn (Datacamp)

  https://datacamp-community-prod.s3.amazonaws.com/f9f06e72-519a-4722-9912-b5de742dbac4

Analytical bayesian inference on Gaussian distribution

• Conjugate Bayesian analysis of the Gaussian distribution

  https://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf

Model Selection (Andrew NG, https://www.coursera.org/learn/machine-learning)

• Diagnosing Bias Vs Variance

  https://www.youtube.com/watch?v=fDQkUN9yw44

• Model Selection And Train Validation Test Sets

  https://www.youtube.com/watch?v=uoTBdCODGvk

Other links

• Visual explanations

  http://setosa.io/#/

• Gaussian processes for regression demo (Toma Peltola)

  http://www.tmpl.fi/gp/

• Seven Myths in Machine Learning Research arxiv

  https://crazyoscarchang.github.io/2019/02/16/seven-myths-in-machine-learning-research/

• Bayesian Methods for Hackers. An introduction to Bayesian methods + probabilistic programming in Python

  https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers