Open-source alternatives to Lambda Ml

Sequence learning toolkit for Python

Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a directed acyclic graph approach, the framework allows users to build intricate models with multiple inputs, outputs, and shared layers, ensuring consistent numerical execution through functional state management. The project distinguishes itself as a multi-backend machine learning

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

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

Python implementation of the rulefit algorithm

Pattern recognition package in Go lang.

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

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

Brain is a JavaScript library for building, training, and running feed-forward neural networks. It implements a multilayer perceptron model designed for pattern recognition and function approximation. The library includes a standalone inference engine that converts trained models into portable JavaScript functions. This allows predictions to be executed in browser or Node.js environments without requiring the original library dependencies. The system supports persistent model management through JSON serialization for saving and loading network weights. It also provides a streaming mechanism

Naive Bayesian Classification for Golang.

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.

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

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).

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

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

DyNet: The Dynamic Neural Network Toolkit

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

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

Fastai is a high-level deep learning library built on PyTorch that provides a unified interface for managing the entire machine learning lifecycle. It functions as a comprehensive training toolkit, abstracting hardware management and automating complex training loops to simplify the construction and execution of neural network models. The framework is distinguished by its notebook-centric development environment and a type-dispatching data pipeline that automatically applies transformations based on input data formats. It emphasizes transfer learning through discriminative layer-wise optimiza

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

Neural Networks written in go

Gorgonia is a Go library that provides an automatic differentiation engine and a computation graph framework for building and training neural networks. It functions as a CUDA-accelerated tensor library and a SIMD-optimized math library, enabling machine learning workflows entirely within the Go ecosystem. The library distinguishes itself through a dual-backend architecture that dispatches neural network operations to either a GPU or CPU depending on CUDA availability at runtime. It constructs differentiable directed acyclic graphs of tensor operations, supports reverse-mode automatic gradient

Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training inherently transparent models and applying post-hoc explanation techniques to make machine learning predictions human-understandable. The framework distinguishes itself by integrating differential privacy into the training of interpretable models to prevent sensitive data from leaking through explanations. It also features a visualization tool for rendering interactive decision paths and model behavior. The library covers model explainability through feature importance calcu

:fourleafclover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)