Displaying 1 to 14 from 14 results

ISLR-python - An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code

  •    Jupyter

This repository contains Python code for a selection of tables, figures and LAB sections from the book 'An Introduction to Statistical Learning with Applications in R' by James, Witten, Hastie, Tibshirani (2013). 2018-01-15: Minor updates to the repository due to changes/deprecations in several packages. The notebooks have been tested with these package versions. Thanks @lincolnfrias and @telescopeuser.

h2o.js - Node.js bindings to H2O, the open-source prediction engine for big data science.

  •    CoffeeScript

This Node.js / io.js module provides access to the H2O JVM (and extensions thereof), its objects, its machine-learning algorithms, and modeling support (basic munging and feature generation) capabilities. It is designed to bring H2O to a wider audience of data and machine learning devotees that work exclusively with Javascript, for building machine learning applications or doing data munging in a fast, scalable environment without any extra mental anguish about threads and parallelism.




ISLR-Python - Notes and implementations in Python for ISLR.

  •    Jupyter

ISLR is a very comprehensive notebook for understanding the basic statistical learning. However, all the sample code and exercises are based on R. Recently, I'd like to reinforce my foundation in related statistics field, therefore, I will go through the entire book with my favorite programming language Python. All the data files are converted to a format that can be handled directly by Python or popular Python packages.


sldm4-h2o - Statistical Learning & Data Mining IV - H2O Presenation & Tutorial

  •    HTML

This repository contains the H2O presentation for Trevor Hastie and Rob Tibshirani's Statistical Learning and Data Mining IV course in Washington, DC on October 19, 2016.

SparseRegression.jl - Statistical Models with Regularization in Pure Julia

  •    Julia

This package relies on primitives defined in the JuliaML ecosystem to implement high-performance algorithms for linear models which often produce sparsity in the coefficients.

dplearn - Learn Deep Learning The Hard Way

  •    Go

Learn Deep Learning The Hard Way. It is a set of small projects on Deep Learning.