Displaying 1 to 20 from 25 results

TensorFlow-Book - Accompanying source code for Machine Learning with TensorFlow

  •    Jupyter

This is the official code repository for Machine Learning with TensorFlow. Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.

python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource

  •    Jupyter

This GitHub repository contains the code examples of the 1st Edition of Python Machine Learning book. If you are looking for the code examples of the 2nd Edition, please refer to this repository instead. What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano.

machine_learning_basics - Plain python implementations of basic machine learning algorithms

  •    Jupyter

This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.




TensorFlow-iOS-Example - Source code for my blog post "Getting started with TensorFlow on iOS"

  •    Swift

This is the code that accompanies my blog post Getting started with TensorFlow on iOS. It uses TensorFlow to train a basic binary classifier on the Gender Recognition by Voice and Speech Analysis dataset.

voice-gender - Gender recognition by voice and speech analysis

  •    R

Read the full article. This project trains a computer program to identify a voice as male or female, based upon acoustic properties of the voice and speech. The model is trained on a dataset consisting of 3,168 recorded voice samples, collected from male and female speakers. The voice samples are pre-processed by acoustic analysis in R and then processed with artificial intelligence/machine learning algorithms to learn gender-specific traits for classifying the voice as male or female.


news-categ-demo - New repo for news categorizer demo showed at blog post (pt-br)

  •    Jupyter

It's a logistic regression demo showed first time at my personal blog linked in description area. Basically, it's a python program for command line that download and process some news content from brasilian sites and use scikit-learn Logistic Regression to classify them in some categories.

dist-lr - A distributed logistic regression system based on ps-lite.

  •    C++

This is a distributed logistic regression system based on ps-lite, which is a distributed parameter server.

Regression - Multiple Regression Package for PHP

  •    PHP

This is a library for regression analysis of data. That is, it attempts to find the line of best fit to describe a relationship within the data. It takes in a series of training observations, each consisting of features and an outcome, and finds how much each feature contributes to the outcome. This library also handles logistic regression, in which the outcomes are booleans. In this case, the regression would give you how much each feature contributes to the probability of the outcome and the prediction process would give you the probability of the outcome for a given new example.

learningjs - javascript implementation of logistic regression/c4.5 decision tree

  •    Javascript

#Update I've made some update on the data loading logic so now it reads in csv-format file. Previous version is still accessible but it's no longer supported. #Introduction Javascript implementation of several machine learning algorithms including Decision Tree and Logistic Regression this far. More to come.

FTRL - R/Rcpp implementation of the 'Follow-the-Regularized-Leader' algorithm

  •    R

R package which implements Follow the proximally-regularized leader algorithm. It allows to solve very large problems with stochastic gradient descend online learning. See Ad Click Prediction: a View from the Trenches for example.

SGDLibrary - MATLAB library for stochastic optimization algorithms: Version 1.0.17

  •    Terra

The SGDLibrary is a pure-MATLAB library of a collection of stochastic optimization algorithms. This solves an unconstrained minimization problem of the form, min f(x) = sum_i f_i(x). The SGDLibrary is also operable on GNU Octave (Free software compatible with many MATLAB scripts). Note that this SGDLibrary internally contains the GDLibrary.