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Open-source Java scientific computing library for JVM and Android platforms, with SciPy-like APIs for use in machine learning and data science workflows. It provides functionality for signal processing, FFT, filters, PSD, and DCT operations.
JSMLT is an open source JavaScript library for education in machine learning. It implements supervised learning algorithms, including SVM, random forest, decision tree, perceptron, neural networks, k-nearest neighbors and logistic regression, and provides visualization examples for exploring how the algorithms work.
A Clojure wrapper for deeplearning4j with added syntactic sugar, aiming to provide a declarative API for machine learning on the JVM. It translates Clojure keywords into Java method calls, making many deeplearning4j configuration options accessible. The project is a work in progress, and configurations have been tested on basic feedforward nets, RNNs and CNNs.
Simple API for Neural Network. Better for image processing with CPU/GPU + Transfer Learning.
JavaScript implementation of the k nearest neighbors algorithm for supervised learning.
KotlinDL is a high-level deep learning API written in Kotlin and inspired by Keras. It uses TensorFlow Java API and ONNX Runtime API for Java, and offers APIs for training models from scratch, importing Keras and ONNX models for inference, and transfer learning.
lambda-ml is a small machine learning library written in Lisp, specifically Clojure, aimed at simple, concise implementations of machine learning techniques and utilities. It covers supervised algorithms such as neural networks, decision trees, ensemble methods, k-nearest neighbors, linear and logistic regression, naive Bayes and random forest, plus unsupervised methods including DBSCAN, hierarchical clustering, k-means and non-negative matrix factorization.
Supports teaching, studying or skills practice with AI-guided learning tools.
Open-source project that provides AI-assisted learning, courses, tutorials, or study support.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Magenta is a research project exploring machine learning in art and music creation, developing deep learning and reinforcement learning algorithms for generating songs, images, and drawings. It also explores tools letting artists and musicians extend their processes with these models. The repository is archived and read-only.
A Python module for metric learning.
Metric Learning Algorithms in Python.
ML-From-Scratch is a GitHub repository that provides bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility, aiming to cover methods from linear regression to deep learning.
ml.js is an open-source machine learning toolset implemented in JavaScript, hosted in the mljs/ml GitHub repository. It provides libraries for both supervised and unsupervised machine learning tasks, alongside usage resources and standalone example code for users.
ml5.js is a JavaScript library providing access to machine learning algorithms and models in the browser, building on TensorFlow.js. It aims to make machine learning approachable for artists, creative coders and students, and is supported by code examples, tutorials and sample data sets. The project is described as currently in development.
A high performance, memory efficient, maximally parallelized ensemble learning, integrated with scikit-learn.
MLKit is a simple machine learning framework written in Swift. Currently it features algorithms dealing with regression, with planned expansion to classification, clustering, recommender systems and deep learning. The project is under active development and is not ready for use in commercial or personal projects, and can be installed via CocoaPods as MachineLearningKit.
Slides, scripts and materials for the Machine Learning in Finance course at NYU Tandon, 2022.
modAL is a modular active learning framework designed for use with the Python programming language. The project includes introductory documentation, overviews of active learning concepts, usage examples, and quickstart guides. It is hosted as an open source repository on GitHub.
Netron is a viewer for neural network, deep learning and machine learning models. It supports formats including ONNX, TensorFlow Lite, PyTorch, TensorFlow, Core ML, OpenVINO, Keras, Caffe, Darknet, Safetensors and NumPy. It is available as a browser version and installers for macOS, Linux and Windows, plus a Python package. Experimental support exists for MLIR, JAX, GGUF and other formats.
Supports teaching, studying or skills practice with AI-guided learning tools.
A parallel deep learning framework hosted on GitHub. The project provides a framework for building and running deep learning workflows with parallel processing support, and includes documentation for setup, available layers, and dependencies.
NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.
