A high-level machine learning and deep learning library for the PHP language.

An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.

A standalone inference server for trained Rubix ML estimators.

Use the famous CIFAR-10 dataset to train a multi-layer neural network to recognize images of cats, dogs, and other things.

An example project that predicts house prices for a Kaggle competition using a Gradient Boosted Machine.

Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.

The original lightweight introduction to machine learning in Rubix ML using the famous Iris dataset and the K Nearest Neighbors classifier.

An example project that predicts risk of credit card default using a Logistic Regression classifier and a 30,000 sample dataset.

Recognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.

Demonstrating unsupervised clustering using the K Means algorithm and synthetic color data.

Build a classifier to predict the outcome of Dota 2 games with the Naive Bayes algorithm and results from 102,944 sample games.

Use the K Nearest Neighbors algorithm to predict the probability of a divorce with high accuracy.