Libraries tagged by data-science

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

Go to Download

PHP-ML - Machine Learning library for PHP

Go to Download

NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, paraphrasing, grammar and spelling correction, keywords and keyphrases extraction, chatbot, product description and ad generation, intent classification, text generation, image generation, code generation, question answering, automatic speech recognition, machine translation, language detection, semantic search, semantic similarity, tokenization, POS tagging, speech synthesis, embeddings, and dependency parsing. It is ready for production, served through a REST API. This is the PHP client for the API. More details here: https://nlpcloud.com. Documentation: https://docs.nlpcloud.com. Github: https://github.com/nlpcloud/nlpcloud-php

Go to Download

Objects that allow you to programmatically connect to Apache Drill.

Go to Download

Deploy your Rubix ML models to production with scalable stand-alone inference servers.

Go to Download

PHP-ML - Machine Learning library for PHP

Go to Download

A PHP client library for accessing MonetDB.

Go to Download

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

Go to Download

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

Go to Download

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

Go to Download

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

Go to Download

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

Go to Download

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

Go to Download

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

Go to Download

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

Go to Download