Displaying 1 to 20 from 36 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.

NakedTensor - Bare bone examples of machine learning in TensorFlow

  •    Python

This is a bare bones example of TensorFlow, a machine learning package published by Google. You will not find a simpler introduction to it. In each example, a straight line is fit to some data. Values for the slope and y-intercept of the line that best fit the data are determined using gradient descent. If you do not know about gradient descent, check out the Wikipedia page.

tensorflow_cookbook - Code for Tensorflow Machine Learning Cookbook

  •    Jupyter

This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

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.

grt - gesture recognition toolkit

  •    C++

The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition. Classification: Adaboost, Decision Tree, Dynamic Time Warping, Gaussian Mixture Models, Hidden Markov Models, k-nearest neighbor, Naive Bayes, Random Forests, Support Vector Machine, Softmax, and more...

highcharts_trendline - HighCharts demo of scatter plot, including a trend line

  •    Javascript

This project illustrates how you can easily add a trendline to your Highchart charts. Note: If you are looking for a more complete implementation, checkout the highcharts-regression plugin.

hdnom - Benchmarking and Visualization Toolkit for Penalized Cox Models

  •    R

hdnom creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data. Browse the vignettes to start.


  •    PHP

PolynomialRegression.php packaged for composer.

linearReg.js - Linear regression with Gradient descent package for NPM.

  •    Javascript

A javascript implementation of linear regression. Loosely based on Prof. Ng's MOOC on Machine Learning.

msaenet - Multi-Step Adaptive Estimation Methods for Reducing False Positive Selection in Sparse Regressions

  •    R

msaenet implements the multi-step adaptive elastic-net (MSAENet) algorithm for feature selection in high-dimensional regressions proposed in Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944> (PDF). Nonconvex multi-step adaptive estimations based on MCP-net or SCAD-net are also supported.

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.

regression - Multivariable regression library in Go

  •    Go

Import the package, create a regression and add data to it. You can use as many variables as you like, in the below example there are 3 variables for each observation. Note: You can also add data points one by one.

linear-regression - Linear regression implemented in Ruby.

  •    Ruby

An implementation of a linear regression machine learning algorithm implemented in Ruby. More details about this example implementation can be found in this blog post.

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