Useful Links, References, Etcetera

Useful Docker Images

• Jupyter-Docker and Documentation
• Tensorflow-Docker and Documentation

Cheatsheets

• Machine Learning Cheatsheet

Online Books

• Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares - Stephen Boyd and Lieven Vandenberghe
• Deep Learning - Ian Goodfellow, Yoshua Bengio, and Aaron Courville
• Storytelling With Data - Cole Nussbaumer Knaflic
• Think Stats: Probability and Statistics for Programmers - Allen B. Downey
• Probabilistic Programming & Bayesian Methods for Hackers - Cam Davidson-Pilon
• The Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, and Jerome Friedman
• An Introduction to Statistical Learning with Applications in R - Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
• Understanding Machine Learning: From Theory to Algorithms - Shai Shalev-Shwartz and Shai Ben-David
• Foundations of Data Science - Avrim Blum, John Hopcroft, and Ravindran Kannan
• A Programmer's Guide to Data Mining: The Ancient Art of the Numerati - Ron Zacharski
• Mining of Massive Datasets - Jure Leskovec, Anand Rajaraman, and Jeff Ullman
• Machine Learning Yearning - Andrew Ng
• Python Data Science Handbook - Jake VanderPlas (Related Jupyter Notebooks)

Good Paper Links

• Most cited deep-learning papers (2012-2016)
• Papers with code (Now with a new state-of-the-art section!)

Tutorials / Education

• Deep Learning Drizzle - a very comprehensive collection of videos covering a wide range on machine learning concepts.
• FastAI Deep Learning Series
• DeepControl.ai - deep reinforcement learning

Blogs

• Curated list of data science blogs

Gaussian Processes collection

• Gaussian Processes for Machine Learning (Book) - Rasmussen and Williams
• Great GP explanation on Distill - Jochen Görtler et al.
• Good explanation of GPs - Katherine Bailey
• Blog post of GP implementation in Python - Chris Fonnesbeck
• Good 1D visualisation of GPs