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A curated GitHub list of TensorFlow experiments, libraries and projects, with sections for tutorials, models, tools, videos, papers and books. It describes TensorFlow as an open source software library for numerical computation using data flow graphs.
BambooAI is an LLM-driven data analyst tool that operates in a persistent Python kernel. It is available as a web app with Docker-isolated execution, supports user-provided model keys, and is compatible with open weights models via provider or local deployment.
ch.at is a lightweight language model chat service accessible through HTTP, SSH, DNS, and an OpenAI-compatible API, plus a public agent board with posts, replies, feeds and search. It requires no JavaScript or tracking, and bots are welcome. State is held in RAM only, so a restart loses all data.
CiteMe is a citation generator that formats references in APA, MLA, Harvard, Chicago, and many other styles. Users paste a DOI, ISBN, URL, or title, and the tool reviews the metadata source before formatting. It searches scholarly databases such as OpenAlex, CrossRef, and PubMed. A free plan is offered, with a Pro tier adding batch PDF import and exports.
CML, or Continuous Machine Learning, is an open-source CLI tool for implementing CI/CD with a focus on MLOps. It is used to automate development workflows, including machine provisioning, model training and evaluation, comparing ML experiments across project history, and monitoring changing datasets. It can generate a visual report with results and metrics automatically on every pull request, using GitLab, GitHub, or Bitbucket.
Conjecture is an open-source project hosted on GitHub that provides tools for scalable machine learning using the Scalding framework. It includes tutorials for creating training data, training classifiers, and evaluating classifiers.
Cornac is an open-source comparative framework for multimodal recommender systems, maintained by PreferredAI and hosted on GitHub. It provides tools for developing and evaluating recommender system models that process multiple types of input data.
DagsHub is an AI platform for managing the machine learning lifecycle, covering dataset curation and annotation, experiment tracking, and model management. It supports unstructured and multimodal data such as text, images, audio, video, and documents. The platform is built on open source tools and formats. DagsHub states it does not currently provide compute resources and relies on the user's own infrastructure.
DataPup is an open-source, AI-assisted, cross-platform database client built with Electron and powered by LangChain for AI assistance. It currently supports ClickHouse and PostgreSQL, with MySQL and SQLite listed as coming soon. It is released under the MIT license.
Datumbox is an open-source machine learning framework written in Java for rapid development of machine learning and statistical applications. It focuses on including many machine learning algorithms and statistical methods and on handling large datasets. The code is licensed under the Apache License, Version 2.0, and releases are available on Maven Central, including version 0.8.2.
A Neural Network Playground is a browser-based educational visualization created by Daniel Smilkov and Shan Carter for experimenting with a real neural network. Users adjust controls to explore how a network learns from data. The project is open sourced on GitHub under the Apache License so its features can be tailored to a topic or lesson.
deepface is a lightweight Python library for face recognition and facial attribute analysis. It supports analysis of age, gender, emotion, and race attributes from facial data. The project is hosted as an open source repository on GitHub.
DeepSeek-R1 is the first-generation reasoning model family from DeepSeek, including DeepSeek-R1-Zero and DeepSeek-R1. The repository describes training via large-scale reinforcement learning and cold-start data, and open-sources the models plus six distilled dense models based on Llama and Qwen.
Eurybia is an open-source tool hosted on GitHub under the MAIF organization. It is designed to monitor model drift over time and secure model deployment processes through data validation functionality, with public documentation covering overview, quickstart, installation, features, and project background.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Feast is an end-to-end open source feature store for machine learning. It allows teams to define, manage, discover, and serve features, delivering structured data to AI and LLM applications during training and inference. Documented use cases include real-time recommendations, fraud detection, risk scoring, and customer segmentation.
Featureform is an open-source virtual feature store hosted on GitHub under the featureform organization. It is designed to turn users' existing data infrastructure into a feature store, with supporting resources including a public website, documentation, community forum, and explanatory material on its core abstraction and unique value proposition.
FEDOT is an open-source framework for automated modeling and machine learning. It provides automatic generative design of machine learning pipelines, with an evolutionary approach supporting classification, regression, clustering and time series prediction. Pipelines are represented as graphs defining connections between data preprocessing and model blocks. It is distributed under the 3-Clause BSD license.
Future AGI is an open-source, self-hostable platform for evaluating, observing, and improving LLM and AI agent applications. It combines tracing, evals, simulations, datasets, a gateway and guardrails on one platform with a feedback loop. The Apache 2.0 core can be self-hosted, and a managed Cloud option is offered.
GPT4All is a private, local AI chatbot tool developed by Nomic. It enables users to run open-source AI models directly on their personal devices without cloud connectivity, with all user data remaining on the local machine.
PandasAI is an open-source library that enables conversational data analysis using large language models and retrieval-augmented generation. It allows users to interact with databases or datalakes supporting SQL, CSV, and parquet file formats.
H2O AutoML is an open-source automatic machine learning tool developed by H2O.ai. It automatically generates features and selections for model building with no coding required. It is part of the H2O.ai platform, with offerings designed for model builders, data scientists, and enterprise developers.
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.
The Kaggle Merck repository, hosted on GitHub under the user zygmuntz, contains code for the Merck challenge hosted on the Kaggle data science platform. It is an open-source project that accepts contributions via GitHub.
