This repo contains guides and other reference material that may be useful when learning how to use a particular tool or programming language.
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How to set up a Jupyter Dashboard server/client
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How to clean out your Docker images, containers and volumes with single commands
- Data science
- Python
- git
- reStructuredText
- Quantitative finance
- Floating-point arithmetic
- How to ask questions the smart way
- Accelerating/Parallelizing
- Software design & modelling
- 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
- 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
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: