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A curated list of Claude Skills, resources, and tools for customizing Claude AI workflows. The repository describes Claude Skills as reusable instruction packages that teach an AI agent to handle a specific class of tasks, each a folder with a SKILL.md file.
H2O is an open-source, distributed, fast, and scalable machine learning platform hosted on GitHub by the h2oai organization. It supports a wide range of machine learning methods including deep learning, gradient boosting, random forest, generalized linear modeling, clustering, dimensionality reduction, rule fitting, support vector machines, stacked ensembles, and automated machine learning.
Machine Learning in MatLab/Octave is a collection of MatLab/Octave implementations of popular machine learning algorithms, including code examples and explanations of the associated mathematics. It covers supervised learning tasks such as regression and classification as well as unsupervised learning.
Machine Learning Module is a GitHub repository that claims to host the best machine learning tutorials on the web. It invites contributions from the community and serves as a collection of learning resources for machine learning.
Awesome Machine Learning with Ruby is a GitHub-hosted curated list of resources for machine learning development using the Ruby programming language. It includes tutorials and machine learning libraries for Ruby users.
cl-online-learning is a Common Lisp library collecting machine learning algorithms for online linear classification. It implements binary classifiers including Perceptron, AROW, SCW-I, logistic regression with SGD or ADAM, and FTRL-Proximal, plus multiclass approaches such as one-vs-rest, one-vs-one, multiclass AROW and softmax with FTRL-Proximal. Command line tools are provided as a roswell script, and the package is available from Quicklisp.
FATE (Federated AI Technology Enabler) is an industrial grade federated learning open source framework that enables enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation, and provides federated learning algorithms including logistic regression, tree-based algorithms, deep learning and transfer learning.
Machine Learning for Software Engineers is a study resource that provides a complete daily study plan for software engineers seeking to become machine learning engineers, structured as a top-down learning path.
A GitHub repository collecting AI learning resources, including over 100 cheat-sheets, free online books, courses, videos and lectures, papers, tutorials, researchers, websites, datasets, conferences, frameworks and tools. Topics include machine learning, deep learning, big data, data science and Python.
Project Based Learning is a GitHub repository or organization with source code, releases, documentation, or project resources.
Approaching (Almost) Any Machine Learning Problem is a GitHub repository or organization with source code, releases, documentation, or project.
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.
A curated GitHub list of federated learning system papers, articles, tutorials, slides and projects. It groups entries such as systems from big tech companies, vertical FL, open-source frameworks, edge and mobile systems, optimization, security and privacy, applications and surveys. Papers reviewed and presented at academic conferences are marked with a graduation cap symbol.
Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms.
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai.
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.
Repository containing references and code for the book Distributed Machine Learning Patterns from Manning Publications by Yuan Tang. The book covers applying patterns to build scalable and reliable machine learning systems, constructing pipelines with data ingestion, distributed training and model serving, and automating tasks with Kubernetes, TensorFlow, Kubeflow and Argo Workflows.
Archived, read-only GitHub repository of free resources for learning machine learning with Python and Jupyter Notebook, including notebooks, courses and other links. It was first posted in 2016 and was archived by the owner on Jun 27, 2022.
Frontend Learning Kit is a GitHub repository or organization with source code, releases, documentation, or project resources.
Gold is a reinforcement learning library for Go. It provides a set of agents for solving challenges in various environments, with composable tooling for creating agents and visualizing their performance. Agents include Q Learning, Deep Q learning with Double Q, REINFORCE, Natural Evolution Strategies, Hindsight Experience Replay and Proximal Policy Optimization, which is marked in progress.
Gym4ReaL is an open-source GitHub repository maintained by Daveonwave. It is a Gymnasium-based benchmarking suite for testing reinforcement learning algorithms on real-world scenarios. The repository includes documentation on supported characteristics, reinforcement learning paradigms, folder structure, contribution guidelines, and licensing terms.
Machine Learning Visualized is a GitHub repository or organization with source code, releases, documentation, or project resources.
Machine Learning Zoomcamp is a free, hands-on course on building, evaluating, and deploying machine learning systems. It covers the path from framing a problem to preparing data, training and evaluating models, and deploying them, using Python, scikit-learn, TensorFlow, PyTorch, FastAPI, Docker, Kubernetes and AWS Lambda. No prior machine learning experience is required.
Code samples for my book "Neural Networks and Deep Learning" [DEEP LEARNING].
