Explore an open-source resource hub for probabilistic machine learning, featuring books, tutorials, and code to help you learn and apply ML concepts.
Learn probabilistic machine learning with curated resources
ProbML is a comprehensive online resource dedicated to probabilistic machine learning. Here, you’ll find a curated collection of books, tutorials, and code examples that make complex machine learning topics more approachable.
Whether you’re a student, researcher, or enthusiast, you can use this site to deepen your understanding of ML concepts through hands-on materials. The site is open-source, so you can also contribute or explore the latest updates from the community.
Discover websites similar to Probml.github.io. Section 1 prioritizes sites with matching domain extensions and/or languages. Section 2 offers worldwide alternatives.
Explore scikit-optimize, a Python library for efficient hyperparameter optimization using sequential model-based methods. Includes guides and docs.
Keras offers user-friendly tools and guides for building deep learning models, making machine learning accessible and efficient for developers of all levels.
Weka offers open source machine learning tools in Java for data mining, analysis, and visualization, making it easy to explore and model data sets.
Explore LightGBM’s official documentation for guides, tutorials, and API references on this fast, distributed gradient boosting framework for machine learning.
Explore YDF documentation to learn how to train, evaluate, and deploy decision forest models like Random Forests using this open-source machine learning library.
Explore detailed documentation and guides for the hdbscan Python library, which helps you find clusters in data using advanced machine learning techniques.
Explore detailed guides and documentation for emcee, a Python tool for Markov chain Monte Carlo (MCMC) sampling and model fitting in data analysis.
Explore XGBoost's official documentation for setup guides, tutorials, and detailed info on this popular machine learning library and its many features.
Ray by Anyscale is an open-source platform that helps you manage and scale AI and machine learning workloads across distributed computing resources.
ELKI is an open-source Java framework for data mining, focusing on clustering and outlier detection with extensible algorithms and benchmarking tools.
Explore UMAP's documentation for detailed guides on dimension reduction, visualization, and machine learning techniques using Python.
Seldon helps businesses manage, deploy, and monitor machine learning and AI models, offering flexible tools for real-time workflows and observability.
Explore Qwen, a platform for advanced large language models and AI tools, offering chat demos and resources for developers and AI enthusiasts.
Foolbox is a Python library for creating adversarial examples to test neural networks, with documentation and tools for machine learning security research.
Integrate machine learning models into Java apps using PMML. Openscoring helps you deploy, manage, and run predictive analytics in real time.
PyTensor offers Python tools to define, optimize, and evaluate complex math expressions with multi-dimensional arrays. Explore docs, guides, and examples.
Test and protect ML models from adversarial attacks
Adversarial Robustness Toolbox offers open-source tools to test, defend, and certify machine learning models against security threats. Python-focused site.
A personal blog sharing insights, tutorials, and thoughts on machine learning concepts and projects. Great for anyone interested in AI and data science.
JAGS is an open-source tool for statistical analysis using Gibbs sampling, helping you build and run Bayesian models for data analysis and research.
Browse and search R package documentation from CRAN, Bioconductor, GitHub, and R-Forge. Find resources, run R code online, and explore package details.
Learn RxJS offers clear examples, explanations, and hands-on resources to help you understand and use RxJS for reactive programming in JavaScript.
Explore the Go programming language with free tutorials, chapters, and downloadable content from the official "The Go Programming Language" book.
Play and Learn offers interactive educational games and resources for kids, making learning fun and engaging through play-based activities.
Explore the work, research, and publications of Alexander Rosenberg Johansen, a machine learning PhD student specializing in wearable biomedical sensors.
Explore resources, solutions, and materials from the book “Mathematics for Machine Learning” to support your study of math and machine learning concepts.
Explore chemistry with an interactive periodic table, reaction solver, and detailed element info—all in one app for students and learners.
StudyCard helps you create, organize, and review digital study cards to improve learning and memory, making studying more efficient and personalized.
Explore PyTorch Geometric's documentation for guides, examples, and tools to build and train Graph Neural Networks using the PyG library and PyTorch.
Access lecture notes and resources from Stanford's CS231n course on deep learning for computer vision, perfect for students and AI enthusiasts.
Tech.io lets you create and share interactive tech tutorials, helping you learn and teach programming skills with a supportive online community.
A Chinese-language resource hub offering open machine learning modeling guides, technical docs, and helpful links for AI development and study.
Join a global data science and machine learning community, access datasets, enter competitions, and use collaborative tools to grow your skills.
Explore resources, guides, and learning materials about Gaussian processes, probabilistic modeling, and inference for students and researchers.
Learn the basics of machine learning in a friendly way, whether you're new to the topic or an experienced developer curious about AI concepts.
Made With ML helps developers learn to design, build, and deploy machine learning applications responsibly with practical lessons and community support.
Stanford's CS229 offers a comprehensive introduction to machine learning, with lectures, materials, and resources for students and self-learners.
Experiment with neural networks in your browser and see how machine learning works through interactive tools and real-time visualizations.
Nyckel lets you quickly build and deploy custom machine learning models—no advanced technical background needed. Fast, secure, and easy to use.
Explore fast artificial neural networks for rapid data processing, learning, and AI development. Ideal for those interested in machine learning and neural networks.
Explore research highlights and expert insights on machine learning and optimization, curated by MadryLab for those interested in the latest advancements.