masdeseiscaracteres / reference_material

This repo contains guides and other reference material that may be useful when learning how to use a particular tool or programming language

Reference material

This repo contains guides and other reference material that may be useful when learning how to use a particular tool or programming language.

Guides

• How to set up a conda environment for data science

• How to set up a Jupyter Dashboard server/client

• How to add new kernels to Jupyter

• How to connect Atom's Hydrogen to remote kernels

• How to profile your code in IPython

• How to customize Jupyter Notebooks

• How to create statically-linked wheels: manylinux, Mac OS X

• How to clean out your Docker images, containers and volumes with single commands

Resources

• Data science
• Python
• git
• reStructuredText
• Quantitative finance
• Floating-point arithmetic
• How to ask questions the smart way
• Accelerating/Parallelizing
• Software design & modelling

Online courses / Video lectures

• Optimization Methods for Business Analytics @ edX
• Linear Algebra by Gilbert Strang @ MIT OCW
• Convex Optimization Short Course by Stephen Boyd
• Machine Learning by Andrew Ng @ Coursera
• Artificial Intelligence, Berkeley's CS 188 @ edX | Spring 2015 | Fall 2014 (videos, slides)
• Machine Learning for Trading, Georgia Tech CS 7646 @ Udacity
• Statistical Learning by Hastie & Tibshirani @ Stanford
• Reinforcement Learning in Finance @ Coursera
• Bayesian Methods in Machine Learning – Fall 2019 - CSE 515T @ Washington University in St. Louis

(Free) Online books

• Applied Mathematical Programming, Bradley, Hax, and Magnanti
• Convex optimization, S. Boyd and L. Vandenberghe
• An Introduction to Statistical Learning with Applications in R, G. James, D. Witten, T. Hastie and R. Tibshirani
• The Elements of Statistical Learning, Data Mining, Inference, and Prediction, T. Hastie, R. Tibshirani and J. Friedman
• Deep Learning, I. Goodfellow, Y. Bengio and A. Courville
• Neural Networks and Deep Learning, M. Nielsen
• Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares, S. Boyd and L. Vandenberghe
• Probabilistic Machine Learning: An Introduction, K. P. Murphy

Books

• Machine Learning: A Probabilistc Perspective [Mendeley]

Learning by example

Cheatsheets

Real-world examples

Collection of libraries/projects whose simplicity makes them ideal as a starting point for building more complex solutions.

• Pandas internals: geopandas, dask
• Cython: pyflux
• Numpy C extensions: py_find_first
• Sphinx: romanvm/sphinx_tutorial
  • Numpy docstring HOWTO: How to document
  • Numpy docstring example: numpy/doc/example
  • Sphinx napoleon extension: sphinxcontrib/napoleon
• Versioneer: warner/python-versioneer
• Continuous integration tools:
  • Appveyor CI: geopandas
  • Travis CI: geopandas
• Entrypoints:
  • entrypoint demo: how to develop a plugin system using entrypoints
• Packaging:
  • pip: how to use a given directory as the root of the source code