Scientific Computing in Python, a set of tutorials and useful examples. All come from my own experience.
Scientific Computing scans a wide spectrum including basic numeric programming, linear algebra, all kinds of distributions, and realizing Maximum Likelihood Estimation (MLE), Expectation-Maximization (EM) algorithm, Monte-Carlo Markov Chain (MCMC) sampling. This tutorial aims to help you to master the basic skills to implement relevant algorithms in python.
In the second part, titled as Practical tricks, I will share some useful code snippets which relate to some confusing points when using numpy, I got into these traps before, so I hope it can serve as a reminder to me and other readers.
- Understanding Numpy and ndarray
- Linear algebra in python
- Ordinal Differential Equation
- Bayesian Probabilistic model
- Frequentist statistical model (MLE, optimization methods, EM, factor analysis, etc)
- stay tuned...
In this Section, I want to share some caveats that numpy user may benefit from: