Resources

Books

No book is required. But we highly recommend two books for this course.

• Fluent Python: Clear, Concise, and Effective Programming, by Luciano Ramalho. Publisher: O'Reilly Media. 2015.
• Designing Data Intensive Applications, by http://dataintensive.net/, The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann. Publisher: O'Reilly Media 2014

Other useful books

• The Practice of Programming (ISBN 0-201-61586-X) by Brian W. Kernighan and Rob Pike, Addison-Wesley, 1999.
• Skiena: The Algorithm Design Manual
• Abelson, Sussmann and Sussmann: SICP and python based online version based on it: http://composingprograms.com/
• High Performance Python: By Micha Gorelick, Ian Ozsvald. Oreilly Media 2014.
• Hacker guide to Python https://thehackerguidetopython.com
• Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation by Andreas Griewank

Papers and other readings

• Best Practices for Scientific Computing
• Python pep8
• An opinionated guide to python style

Git

• GitHub Videos and Training
• GitHub Interactive Tutorial
• git - the simple guide
• Git Reference
• Git Cheat Sheet
• Git Immersion Tutorial

Python

• python
• anaconda

C/C++

• C Tutorial
• C++ Tutorial
• C++ Cheat Sheet
• C++ Quick Reference

Unix-Related

• Basic Computing Tools

Automatic Differentiation

• CS231n Convolutional Neural Networks for Visual Recognition

Other useful documents

• Using Linux Subsystem on Windows 10