Open-source alternatives to Go Galib

XGBoost is a distributed machine learning library for implementing scalable gradient boosting decision trees used for regression, classification, and ranking. It functions as a predictive model framework and a cross-language toolkit, providing a core implementation with native bindings for Python, R, Java, Scala, and C++. The system is designed as a GPU-accelerated library that utilizes CUDA and NCCL to speed up the training of decision tree ensembles. It operates as a distributed framework capable of scaling training and prediction across multi-node clusters and GPU environments to process m

A C library for product recommendations/suggestions using collaborative filtering (CF)

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.

Caffe is a high-performance deep learning framework designed for training and deploying deep neural networks. It functions as a machine learning engine and a convolutional neural network library, providing a C++ backend to accelerate computations on both GPUs and CPUs. The system includes a specialized toolset for computer vision, enabling tasks such as object detection, semantic segmentation, and large-scale image retrieval. It supports the deployment of pre-trained models for image and scene recognition, as well as the ability to fine-tune neural network weights for specialized tasks. The

On-line Machine Learning in Go (and so much more)

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.

Machine learning compiler in Go. A from-scratch tinygrad port: graph-rewrite IR, autodiff as a compiler pass, zero-CGO WebGPU backend.

A lightweight C++ machine learning library for embedded electronics and robotics.

Aim is an open-source platform for logging, visualizing, and comparing machine learning training runs and LLM traces. It provides a remote tracking server and a comparison UI, functioning as an ML experiment tracker, AI workflow logger, and LLM trace recorder that captures prompts, generations, and tool calls from AI applications. The platform distinguishes itself through a run-based data model with local SQLite storage, real-time metric streaming, and a plugin-based explorer system that supports specialized visual analysis of metrics, images, audio, and text. It offers a Python SDK with cont

Mmlspark is a distributed framework for executing machine learning models, data transformations, and AI service integrations across Apache Spark clusters. It functions as a distributed machine learning library and pipeline orchestrator, allowing users to integrate pre-trained cognitive services and custom models into large-scale batch and streaming workflows. The project is distinguished by its ability to incorporate external AI services and web APIs directly into big data pipelines for text and vision analysis. It provides a scalable model training framework that coordinates gradient boostin

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

Hub is a multimodal AI data lake and vector database designed for storing and querying embeddings, text, audio, and images. It functions as a dataset version control system and a machine learning data streaming engine to support large-scale model training. The system utilizes a serverless PostgreSQL vector store to index high-dimensional embeddings for semantic search. It provides a visual interface for inspecting multimodal datasets and viewing annotations such as bounding boxes and masks. The platform handles cloud-agnostic storage synchronization and implements lazy, compressed data strea

Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

Accelerated deep learning R&D

CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression, classification, and ranking tasks. It functions as a high-performance framework that provides a categorical data processor for transforming non-numeric features, a distributed trainer for large-scale datasets, and GPU acceleration to speed up model construction. The library distinguishes itself through native handling of categorical data and text features, removing the need for manual encoding. It includes a specialized model interpretability tool that leverages SHAP values and featu

A small machine learning library written in Lisp (Clojure) aimed at providing simple, concise implementations of machine learning techniques and utilities.

🔥 Cogitare - A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python

Pattern recognition package in Go lang.

dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and utilities for building predictive modeling applications and performing statistical analysis on large datasets within native C++ environments. The project functions as a binding library that wraps low-level C++ machine learning algorithms into high-level Python scripting interfaces. This allows for the integration of high-performance native implementations with Python for machine learning development. The framework covers the implementation of predictive models, the execution of mach

This project is an educational toolkit that provides implementations of fundamental machine learning algorithms built from scratch. By avoiding high-level library abstractions, it serves as a pedagogical reference for understanding the mathematical foundations and core mechanics of supervised learning, unsupervised learning, and reinforcement learning models. The repository distinguishes itself through a modular approach to model construction, allowing users to build custom neural networks by chaining independent functional blocks. It covers a wide range of techniques, including gradient-base

CONTRIBUTORS WELCOME Generalized Additive Models in Python

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

DyNet: The Dynamic Neural Network Toolkit