Machine Learning
Showing 36 of 1845 projects
A modular framework for Torch providing abstractions for datasets, engines, meters, and logs to encourage code re-use.
A benchmarking platform for molecular generation models, providing datasets, implementations, and evaluation metrics for drug discovery research.
A pretrained modeling library for Keras 3 offering simple, flexible, and fast access to models for text, image, and audio tasks.
A TensorFlow implementation of QANet for machine reading comprehension on the SQuAD dataset.
A flexible Python framework for developing, training, and evaluating conversational AI agents in single or multi-agent environments.
Sparkling Water provides H2O functionality inside Spark cluster
An R interface for Apache Spark that enables distributed data processing, machine learning, and SQL queries using familiar R syntax.
A Python implementation of Restricted Boltzmann Machines for binary factor analysis and collaborative filtering.
A lightweight TensorFlow library for training and evaluating Generative Adversarial Networks (GANs).
A scikit-learn compatible Python module for multi-label classification tasks.
A scalable, hardware-accelerated neuroevolution toolkit built on JAX for parallel training across TPUs/GPUs.
Combines the ease of use of scikit-learn with the power of Theano/Lasagne
A curated collection of research papers on molecular and material design using generative AI and deep learning techniques.
A fast, ergonomic machine learning library for Rust with broad algorithm coverage and WASM-first defaults.
A unified Python interface for constructing and managing workflows across engines like Argo Workflows, Tekton Pipelines, and Apache Airflow.
A curated list of open-source and commercial tools for labeling and managing datasets across images, audio, time series, and text.
A uniform interface to run deep learning models from multiple frameworks like TensorFlow, PyTorch, and Keras in C++ and Python.
A Recurrent Neural Network library for Torch7's nn, providing RNN, LSTM, GRU, and other sequence modeling modules.
A strongly-typed Scala API for TensorFlow, providing functionality similar to the official Python API with additional features.
A Python API for the Argoverse dataset, providing tools for 3D tracking, motion forecasting, and HD map interaction for autonomous vehicle research.
An open-source simulator for experimenting with and advancing self-driving AI, accessible to anyone with a PC.
A Python implementation of Factorization Machines for recommendation and classification tasks using stochastic gradient descent with adaptive regularization.
A CPU and GPU-accelerated machine learning library optimized for high-performance computing.
A collaboratively maintained, reverse-chronological list of datasets and corpora for natural language processing tasks.
A curated list of awesome tutorials, blogs, and projects for the CARLA autonomous driving simulator.
A JAX-based library providing accelerated reinforcement learning environments with full compatibility to the classic gym API.
A Ruby machine learning library with a Scikit-Learn-like interface for classification, regression, clustering, and dimensionality reduction.
A Swift camera framework for iOS that simplifies AVFoundation usage and integrates CoreML models for real-time object recognition.
TensorFlow implementation of an attention-based neural image caption generator that focuses on relevant image parts while generating words.
A Go interface for importing and executing pre-trained ONNX neural network models without framework dependencies.
An evolutionary optimization library for Go implementing genetic algorithms, particle swarm optimization, differential evolution, and other algorithms.
A C++ recurrent neural network library for sequence learning problems, specializing in online handwriting prediction and synthesis.
Fast multilayer perceptron neural network library for iOS and Mac OS X using Apple's Accelerate Framework.
MatLab/Octave implementations of popular machine learning algorithms with detailed mathematical explanations and code examples.
GPU-accelerated Python implementation of six fundamental deep learning algorithms using CUDA libraries.
An open-source framework for building multi-modal geospatial ML models that fuse satellite, drone, and weather data for agriculture and sustainability insights.
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