Machine Learning
Showing 36 of 1845 projects
An all-in-one framework for training state-of-the-art computer vision models, covering pretraining, fine-tuning, and distillation.
Machine learning with dataframes
A multi-language library providing implementations of common supervised machine learning evaluation metrics.
A high-performance machine learning library for Haskell that leverages algebraic structures for parallel, online, and fast cross-validation training.
Hyperopt-sklearn automates hyperparameter optimization and model selection for scikit-learn machine learning pipelines.
A Python library for automated hyperparameter optimization and model evaluation with TensorFlow, Keras, and PyTorch.
Python implementation of the Boruta all-relevant feature selection method with scikit-learn compatibility.
An accelerated machine learning framework for Go, offering a PyTorch/Jax/TensorFlow-like experience with support for CPUs, GPUs, TPUs, and WASM.
A fast Support Vector Machine (SVM) library that leverages GPUs and multi-core CPUs for high-performance machine learning.
A curated collection of 60 ChatGPT prompts for data science tasks, from model building to code explanation.
A Python package for concise, transparent, and accurate predictive modeling with sklearn-compatible interpretable models.
A Go machine learning library with online learning capabilities and a variety of implemented models.
A deprecated repository for community-contributed Keras extensions like layers, activations, and loss functions.
An open-source Python repository providing around 40 feature selection algorithms for machine learning applications.
A Python library that automatically extracts schema, statistics, and sensitive entities (PII/NPI) from datasets.
Elephas is a Keras extension for distributed deep learning on Apache Spark, enabling data-parallel training at scale.
A high-level Deep Learning API for JVM and Android developers, written in Kotlin and inspired by Keras.
A Bayesian optimization software package for automatically running experiments to minimize an objective in as few runs as possible.
A state-of-the-art diffusion model for predicting how small molecules (ligands) bind to proteins.
A Python framework for building real-time data pipelines and event-driven microservices on Apache Kafka using a Streaming DataFrame API.
MLeap is a portable execution engine for deploying machine learning pipelines from Spark and Scikit-learn without their runtime dependencies.
A machine learning framework for iOS that records location and motion data and detects user activity types like walking, cycling, and transport modes.
A TensorFlow library for building Graph Neural Networks with support for heterogeneous graphs and scalable data processing.
A curated list of resources for Document Understanding (DU), covering research, datasets, tools, and applications in Intelligent Document Processing.
A Python library for agile data preparation workflows that works with Pandas, Dask, cuDF, Dask-cuDF, Vaex, and PySpark.
MLBox is a powerful Automated Machine Learning python library.
A Python library for time series forecasting using scikit-learn compatible machine learning models.
A Python library for time series forecasting using scikit-learn compatible machine learning models.
A curated collection of must-read academic papers on knowledge representation learning and knowledge embedding, with an associated open-source toolkit.
A C++-based high-performance parallel environment execution engine for vectorized reinforcement learning simulations.
A comprehensive Python library for generating and analyzing multi-class confusion matrices with extensive statistical metrics.
A foundational PyTorch library for training deep learning models, serving as the core engine for the OpenMMLab ecosystem.
A Ruby library implementing the ID3 algorithm for decision tree learning with support for continuous and discrete datasets.
A software implementation of factorization machines for estimating interactions between categorical variables in large datasets.
A model-agnostic toolkit for exploring and explaining the behavior of complex machine learning models in R and Python.
A comprehensive benchmark suite for evaluating speed, throughput, and resource utilization of big data frameworks like Hadoop, Spark, and streaming engines.
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