kirk86/pattern_classification
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
Discovered public repositories for kirk86 in the GitHub catalog.
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
Repository of my thesis "Understanding Random Forests"
Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.
A Python to C compiler
Probabilistic Modeling Toolkit for Matlab/Octave.
An example application using Word2Vec. Given a list of words, it finds the one which isn't 'like' the others - a typical language understanding evaluation task.
Free programming books
Public repository.
Machine learning algorithms I implemented using different programming languages.
Trying to complete over 100 projects in various categories in Python. Fork to learn any new language.
Public repository.
Mission: To provide a high-quality open content data structures textbook that is both mathematically rigorous and provides complete implementations.
Source Material for a course on Programming targeted at scientists
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 ;)
Code used to implement various stochastic intensity models for univariate and multivariate credit risk models.
Contains LaTeX, SciPy and R code providing solutions to exercises in Elements of Statistical Learning (Hastie, Tibshirani & Friedman)
Contains lecture notes for several Sydney University advanced mathematics courses. Contributions welcomed!
lecture notes of a Cambridge mathmo
Introduction to Analysis - Homework and Lectures
Math 116 Lecture Notes
Deep Learning (Python, C/C++, Java)
Code for Deep Learning class at Google
Deep learning made easy
Deep learning code by Hinton
Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.
Deep Learning Tutorial notes and code. See the wiki for more info.
Robust and secure watermarking scheme based on singular values replacement (SVD & DWT)
Content Based Image Retrieval Techniques (e.g. knn, svm using MatLab GUI)
Small Hospital Managements System
Content Image Retrieval System