Displaying 1 to 12 from 12 results

Accord.NET - Machine learning, Computer vision, Statistics and general scientific computing for .NET

  •    CSharp

The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

Clairvoyant - Software designed to identify and monitor social/historical cues for short term stock movement

  •    Python

Using stock historical data, train a supervised learning algorithm with any combination of financial indicators. Rapidly backtest your model for accuracy and simulate investment portfolio performance.During the testing period, the model signals to buy or sell based on its prediction for price movement the following day. By putting your trading algorithm aside and testing for signal accuracy alone, you can rapidly build and test more reliable models.

svmjs - Support Vector Machine in Javascript (SMO algorithm, supports arbitrary kernels) + GUI demo

  •    Javascript

svmjs is a lightweight implementation of the SMO algorithm to train a binary Support Vector Machine. As this uses the dual formulation, it also supports arbitrary kernels. Correctness test, together with MATLAB reference code are in /test. Corresponding code is inside /demo directory.

fashion - The Fashion-MNIST dataset and machine learning models.

  •    R

Training AI machine learning models on the Fashion MNIST dataset. Fashion-MNIST is a dataset consisting of 70,000 images (60k training and 10k test) of clothing objects, such as shirts, pants, shoes, and more. Each example is a 28x28 grayscale image, associated with a label from 10 classes. The 10 classes are listed below.

spampy - Spam filtering module with Machine Learning using SVM (Support Vector Machines).

  •    Python

Spam filtering module with Machine Learning using SVM. spampy is a classifier that uses Support Vector Machines which tries to classify given raw emails if they are spam or not. Support vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier.

svm-spam-classifier-javascript - 🍃 Spam Classifier with Data Preparation and Support Vector Machine (SVM)

  •    Javascript

This example project demonstrates how support vector machine (SVM) may be used to solve a classification problem (spam filter) in JavaScript. The SMS Spam Collection Dataset from kaggle is used for the purpose of training and testing the algorithm. Before training the algorithm, the data set is prepared with common practices to finally extract a feature vector for each SMS. Furthermore, svm.js is used for a ready to go SVM implementation. As alternative, uncomment the code to use Naive Bayes classifier instead of SVM from the natural library.

2018-MachineLearning-Lectures-ESA - Machine Learning Lectures at the European Space Agency (ESA) in 2018

  •    Jupyter

In 2018, The European Space Agency (ESA) organized a series of 6 lectures on Machine Learning at the European Space Operations Centre (ESOC). This repository contains the lectures resources: presentations, notebooks and links to the videos (presentation and hands-on).

vehicle-tracking-2 - A vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM)

  •    Jupyter

Created a vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM). Optimized and evaluated the model on video data from a automotive camera taken during highway driving. To run any notebook properly, copy the jupyter notebooks from the /ipynb folder to the root directory. This is so that each notebook sees relevant files, the most relevant files being the python classes.

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