Displaying 1 to 20 from 25 results

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...




kmcuda - Large scale K-means and K-nn implementation on NVIDIA GPU / CUDA

  •    Jupyter

K-means implementation is based on "Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup". While it introduces some overhead and many conditional clauses which are bad for CUDA, it still shows 1.6-2x speedup against the Lloyd algorithm. K-nearest neighbors employ the same triangle inequality idea and require precalculated centroids and cluster assignments, similar to the flattened ball tree. Technically, this project is a shared library which exports two functions defined in kmcuda.h: kmeans_cuda and knn_cuda. It has built-in Python3 and R native extension support, so you can from libKMCUDA import kmeans_cuda or dyn.load("libKMCUDA.so").

prominentcolor - golang package to find the K most dominant/prominent colors in an image

  •    Go

The Kmeans function returns the K most dominant colors in the image, ordered in the order of dominance. which takes an image and returns the K dominant colors, sorted by the most frequent one first.

osm-data-classification - OpenStreetMap Data Classification

  •    Python

Our first idea was to answer to this question: can we assess the quality of OpenStreetMap data? (and how?). This project is dedicated to explore and analyze the OpenStreetMap data history in order to classify the contributors.

node-geocluster - find clusters in coordinates

  •    Javascript

geocluster finds clusters in sets of coordinates. It's a port of S-means by Brian Hann (which itself is a stdev-driven form of K-means), but with two dimensions, Earth-geodesic distance and properly working code. coordinates is an Array of [lat, lon] pairs. bias is a factor the standard deviation gets multiplied with, which acts as threshold to determine if a coordinate belongs to a cluster.


medoids - K-medoids implementation.

  •    Python

K-medoids implementation.

kmpp - k-means clustering algorithm with k-means++ initialization.

  •    Javascript

When dealing with lots of data points, clustering algorithms may be needed in order to group them. The k-means algorithm partitions n data points into k clusters and finds the centroids of these clusters incrementally. The basic k-means algorithm is initialized with k centroids at random positions.

clusters - k-means clustering in Javascript

  •    Javascript

Clusters identifies clusters in data using the k-means algorithm implemented in JavaScript. For more on K-means, check out this blog post. The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

colorz - :art: A k-means color scheme generator.

  •    Python

A k-means color scheme generator. or just move colorz.py to somewhere in your $PATH. If you do the latter, you must install the dependencies in the following section manually.

clustering.js - Clustering algorithms implemented in Javascript for Node.js and the browser

  •    Javascript

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

kMeans.js - Simple Javascript implementation of the k-means algorithm, for node.js and the browser

  •    CoffeeScript

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

gouda - Golang Utilities for Data Analysis

  •    Go

A collection of Golang libraries implementing various techniques for data analysis, including machine learning. This is work in progress. Expect breaking changes. Embrace for impact.

tensorflow-101 - TensorFlow 101: Introduction to TensorFlow

  •    Jupyter

In this repository, source codes will be shared while capturing "TensorFlow 101: Introduction to Deep Learning" online course published on Udemy. The course consists of 18 lectures and includes 3 hours material.

KMeans_elbow - Code for determining optimal number of clusters for K-means algorithm using the 'elbow criterion'

  •    Jupyter

"The idea behind k-Means Clustering is to take a bunch of data and determine if there are any natural clusters (groups of related objects) within the data. The k-Means algorithm is a so-called unsupervised learning algorithm. We don't know in advance what patterns exist in the data -- it has no formal classification to it -- but we would like to see if we can divide the data into groups somehow.

Machine_Learning - Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks

  •    Jupyter

Esse repositório foi criado com a intenção de difundir o ensino de Machine Learning em português. Os algoritmos aqui implementados não são otimizados e foram implementados visando o fácil entendimento. Portanto, não devem ser utilizados para fins de pesquisa ou outros fins além dos especificados.





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