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Open-source, low-code machine learning library in Python that automates machine learning workflows.
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mlsys.stanford.edu
Stanford MLSys Seminar Series is a seminar series on the frontier of machine learning and systems. Its 2025 "Hidden Layer" series schedule lists talks, and earlier limited series covered systems for machine learning and foundation models. Stanford students could sign up for associated classes, and an email list provides speaker and livestream notifications.
github.com
Swift for TensorFlow is an archived GitHub repository describing an experiment in a next-generation machine learning platform that combined machine learning, compilers and differentiable programming. It was archived in February 2021, and the repository is read-only; the site states it will not receive further updates.
tensorflow.org
TensorFlow is an end-to-end open source machine learning platform. It provides a flexible ecosystem of tools, libraries, and community resources for machine learning development, including libraries for web, mobile, edge, and production ML pipelines, as well as pre-trained models, datasets, and educational resources for users.
github.com
TPOT (Tree-based Pipeline Optimization Tool) is a Python automated machine learning tool that optimizes machine learning pipelines using genetic programming. The project underwent a major refactoring, merging what was once called TPOT2 into the main package, with new features including genetic feature selection, multi-objective optimization and an expanded method of defining search spaces.
tribuo.org
Tribuo is a machine learning library written in Java, developed by Oracle Labs' Machine Learning Research Group and released under the Apache 2.0 license. It supports classification, regression, clustering, anomaly detection and multi-label classification, and provides interfaces to libraries such as XGBoost and TensorFlow along with ONNX model exchange support.
Sebastian Thrun’s Introduction To Machine Learning provides AI-assisted learning, courses, tutorials, or study support.
deep-learning-drizzle.github.io
Deep Learning Drizzle is a collection of links to university and instructor lecture materials on deep learning and related machine learning topics. It lists courses by title, instructor or institution, course webpage, lecture videos and year. Topics include neural networks, natural language processing, computer vision, reinforcement learning, Bayesian deep learning, medical imaging and graph neural networks.
github.com
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.
flashcardmachine.com
Flashcard Machine is a free online service for creating, studying and sharing web-based study flashcards. It hosts a library of millions of user-created flashcard sets, and offers features including collaborative editing, image and audio support, multiple study modes and educational games. Users can access the service via mobile applications for iOS, Android and Kindle Fire devices, and share sets privately or publish them to the public library.
github.com
InterpretML is an open-source package that collects machine learning interpretability techniques. It allows training interpretable glassbox models and explaining blackbox systems, to understand a model's global behavior or the reasons behind individual predictions. It includes the Explainable Boosting Machine, developed at Microsoft Research, plus other glassbox models and blackbox explainers.
github.com
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
github.com
neonrvm is an open-source machine learning library for performing regression tasks using the Relevance Vector Machine (RVM) technique. It is written in Python and available on GitHub. The library aims to provide efficient implementations for RVM-based regression.
deepmarkets.substack.com
An article by Cavin on Deep Markets discussing machine-generated content and creativity. It describes technology as leverage over creation: writing, the printing press, the internet, digital tools and machine learning. It also covers synthetic text from large language models such as OpenAI's GPT-3, published in summer 2020.
github.com
Awesome AutoML Papers is a GitHub-hosted curated list focused on automated machine learning (AutoML). It aggregates academic papers, articles, tutorials, slide decks, and related projects in the AutoML field, serving as a centralized reference for users exploring automated machine learning research and developments.
bishopbook.com
A book offering a comprehensive introduction to central ideas underpinning deep learning, intended for newcomers to machine learning and those already experienced in the field. It covers concepts relating to contemporary architectures and techniques and includes a self-contained introduction to probability theory. Hardback, PDF eBook, and a free-to-use online version are listed as access options.
jinglescode.github.io
Hong Jing (Jingles) is a data scientist and Alibaba PhD student who writes about machine learning and neuroscience. The site collects blog articles on topics such as reinforcement learning agents, learned optimizers, convolutional layers, transformer models and decoding brain signals.
home.work.caltech.edu
An introductory machine learning course taught by Caltech Professor Yaser Abu-Mostafa, recorded from a live broadcast and available as a free online MOOC. It covers basic theory, algorithms and applications across 18 lectures, with homework sets and a final exam. Prerequisites are basic probability, matrices and calculus.
mlberkeley.substack.com
Alien Dreams: An Emerging Art Scene - ML@B Blog provides machine-learning models, research, training resources, or evaluation tools.
github.com
AutoKeras is an AutoML library for deep learning built on Keras, developed by DATA Lab at Texas A&M University. The project describes its goal as making machine learning accessible to everyone. It can be installed with pip, and the documentation states it is currently only compatible with Python 3.7 or later and TensorFlow 2.8.0 or later.
bigml.com
BigML is a machine learning platform for classification, regression, time series forecasting, cluster analysis, anomaly detection, association discovery, topic modeling and principal component analysis. Models can be used for predictions through remote services or exported and embedded into applications. A three-day free trial gives access to all functionality.
github.com
Cogitare is a modern, fast, modular deep learning and machine learning framework for Python built on top of PyTorch, with a user-friendly interface for beginners and advanced toolsets for experts.
dagshub.com
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.
github.com
Deeplearning4j is an open-source suite of tools for deploying and training deep learning models using the Java Virtual Machine (JVM). It supports model import from Keras, TensorFlow, and ONNX/PyTorch, includes a modular C++ math library with a corresponding Java library built on top, and offers SameDiff, a library for deep learning operations similar to PyTorch and TensorFlow.