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Python/PyMC3 versions of the programs described in Doing bayesian data analysis by John K. Kruschke
A python tutorial on bayesian modeling techniques (PyMC3)
DBSCAN clustering algorithm on top of Apache Spark
An implementation of DBSCAN runing on top of Apache Spark
(R package) Analyze High-Dimensional Data Using Discrete Morse Theory
Implementation of Mapper algorithm for Topological Data Analysis
KeplerMapper is a Python class for visualization of high-dimensional data and 3-D point cloud data.
Stock options, RSUs, taxes — a guide for humans
Input-aware cuBLAS/clBLAS implementation for better performance
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Simple tutorials using Google's TensorFlow Framework
Computation using data flow graphs for scalable machine learning
Efficiently computes derivatives of numpy code.
This is a Experimental version of OpenCL by AMD Research, we now recommend you to use The official BVLC Caffe OpenCL branch is over at Caffe branch now at https://github.com/BVLC/caffe/tree/opencl
A powerful machine learning algorithm utilizing Q-Learning and Neural Networks, implemented using Torch and Lua.
The Arcade Learning Environment (ALE) -- a platform for AI research.
OpenCL library to train deep convolutional neural networks
High performance <canvas> rendering for React components
Bare bones introduction to machine learning from linear regression to convolutional neural networks using Theano.
Sass makes CSS fun again.
Framework for experimenting with Recurrent Neural Network architectures, such as (simple, fully connected) RNNs, (Projected) Long Short-Term Memory networks, etc.
Scikit-Learn tutorial material for Scipy 2015