## Linkfest

I am going to try and start posting regularily again, including proper blog posts and not just linkfests.

## Statistics and Machine Learning

- A Fervent Defense of Frequentist Statistics
- Neural nets overview
- Bayesian Statistical Analysis with PyMC
- Machine Learning and Probabilistic Graphical Models: Course Materials
- A visual explanation of conditional probability
*Data Analysis: The Hard Parts*- New machine learning MOOcs at Udacity from Georgia Tech
- Visualizing distributions of data
- Interview with Yann LeCun, Deep Learning Expert, Director of Facebook AI Lab

## ML/Stats Package of the Week

## Paper(s) of the Week

## Programming

- Python Tools for Machine Learning (really nice and worthwhile overview)
- Obtaining, Scrubbing, and Exploring Data at the Command Line
- Getting Started With Scripted Geo-Data Processing (PostgreSQL, PostGIS, Python, and a little OGR) | Geo Perspectives
- High Performance Python at PyDataLondon 2014
- Data-Intensive Text Processing with MapReduce
- Drawing histograms in PostgreSQL
- Building Responsive Visualizations with D3.js
- pandasql (pandas with SQL syntax)
- Writing Code That Doesn’t Suck
- launchrocket (a Mac PrefPane to manage all your Homebrew-installed services)
- More about interactive graphs using Python, d3.js, R, shiny, IPython, vincent, d3py, python-nvd3
- The Best of Python in 2013
- Vowpal Wabbit: the redis of the data science community
- Parallel Monte Carlo using Scala
- Steven Loria | Python Best Practice Patterns
- Using IPython Profiles for More Effective Interactive Sessions
- Introducing the Pig Cheat Sheet
- rMaps Mexico map
- How to learn Haskell
- Easily distributing a parallel IPython Notebook on a cluster
- Example of why to use monads - what they can do