Research & Education

99-ML-Learning-Projects offers a curated repository of 99 machine learning projects designed for individuals eager to learn machine learning by actively coding and building. The platform emphasizes a hands-on approach, providing exercises and solutions that are useful for learners at various stages. It encourages community contributions, allowing users to propose new exercises and solutions. The project aims to foster an open and friendly open-source collaboration environment, with current offerings including projects in General-Purpose Machine Learning, Computer Vision, Natural Language Processing, and Bayesian Naive Bayes Classification. It also provides refreshers and cheatsheets for essential libraries like Numpy and Pandas, and lists required dependencies for project execution.

a-PyTorch-Tutorial-to-Super-Resolution offers a comprehensive PyTorch tutorial focused on implementing photo-realistic single image super-resolution using Generative Adversarial Networks (GANs). It serves as an educational resource for understanding GANs and their application in image enhancement, specifically for quadrupling image dimensions. The tutorial covers concepts like residual connections, sub-pixel convolution, and perceptual loss, guiding users through the implementation of both SRResNet and SRGAN models. It assumes basic knowledge of PyTorch and convolutional neural networks, making it suitable for those looking to deepen their understanding of advanced deep learning techniques for image processing.

Atomwise leverages an advanced AI superplatform to revolutionize drug discovery by exploring the vast universe of chemical space. This platform is designed to identify novel, drug-like molecules that might otherwise remain undiscovered. By applying machine learning techniques, Atomwise aims to enhance the drug discovery process, particularly in the development of small-molecule drugs. The company focuses on creating programs that deliver first- and best-in-class potential, especially within immune and inflammatory diseases. Their approach is driven by a world-class team of scientists and engineers dedicated to redefining how new medications are brought to light.

AI-Expert-Roadmap is a comprehensive, open-source resource designed to guide individuals on their journey to becoming an Artificial Intelligence expert. Hosted on GitHub, it provides detailed charts and recommended technologies for various AI-related fields, including data science, machine learning, deep learning, data engineering, and big data engineering. The roadmap was initially created for AMAI GmbH's new employees to accelerate their AI expertise but is openly shared with the community. It emphasizes understanding why certain tools are better suited for specific cases rather than just following trends. An interactive version with links for each bullet point is available, and users can star and watch the GitHub repository for updates and new content.

awesome-persian-nlp-ir is a comprehensive, curated list dedicated to Persian Natural Language Processing (NLP) and Information Retrieval (IR) tools and resources. This GitHub repository serves as a central hub for researchers, developers, and enthusiasts interested in the field, segmenting its content into five main categories: Tools, Datasets, Models, Repositories, and Papers and Books. It aims to consolidate various research efforts and practical applications related to Persian NLP, making it easier for users to discover and utilize relevant resources. The repository encourages community contributions to ensure its continued growth and relevance, providing guidelines for new submissions.

awesome-quantum-machine-learning is a comprehensive, curated list designed to provide a deep dive into the world of quantum machine learning. It covers fundamental concepts such as quantum mechanics and quantum computing, alongside advanced topics like quantum algorithms, quantum neural networks, and quantum statistical data analysis. The resource includes detailed descriptions of various quantum machine learning algorithms, study materials, and a collection of relevant libraries and software. It also features sections on quantum programming languages, tools, and hot topics in the field, making it an invaluable resource for anyone looking to explore or advance their knowledge in quantum machine learning, from basic principles to cutting-edge research.

Awesome-Efficient-LLM is a comprehensive, curated list of resources focused on efficient large language models (LLMs). This open-source project provides researchers and engineers with a centralized hub for papers and projects related to optimizing LLMs. The list is organized into various sub-areas, including Network Pruning / Sparsity, Knowledge Distillation, Quantization, Inference Acceleration, Efficient MOE, Efficient Architecture of LLM, KV Cache Compression, Text Compression, Low-Rank Decomposition, Hardware / System / Serving, Efficient Fine-tuning, Efficient Training, Survey or Benchmark, and Reasoning Model. Users can easily navigate through these categories to find relevant papers, with recent additions highlighted on the main page. The project also encourages community contributions, allowing users to submit new papers or update existing details via pull requests or email, ensuring the list remains current and comprehensive.

LongBench is an open-source evaluation tool designed to rigorously assess the capabilities of Large Language Models (LLMs) in processing and reasoning over extensive contexts. LongBench v2, the latest iteration, features context lengths ranging from 8k to 2M words, presenting a significant challenge even for human experts. It covers six major task categories including single-document QA, multi-document QA, long in-context learning, long-dialogue history understanding, code repo understanding, and long structured data understanding. The benchmark consists of 503 challenging multiple-choice questions, ensuring reliable evaluation. Data is collected from nearly 100 highly educated individuals, undergoing both automated and manual review to maintain high quality and difficulty. LongBench aims to provide a reliable standard for developing future superhuman long-context AI systems.

LLM-scientific-feedback is an open-source project that leverages large language models, specifically GPT-4, to provide comprehensive feedback on research papers. The tool offers an automated pipeline to analyze full PDF documents of scientific papers and generate comments. Empirical analysis has shown that the overlap between GPT-4's feedback and human peer reviewer feedback is comparable to the overlap between two human reviewers. It is particularly beneficial for researchers, especially those who are junior or in under-resourced settings, to receive timely feedback. While it excels in certain areas like suggesting additional experiments, it also has limitations, such as struggling with in-depth critique of method design. The project includes Python source code and instructions for setting up PDF parsing and LLM feedback servers.

awesome-2vec is a comprehensive, curated list of 2vec-type embedding models, hosted as an open-source project on GitHub. This repository serves as a central hub for researchers and developers to discover and explore a wide array of embedding models, including popular ones like word2vec, doc2vec, and node2vec, as well as more specialized models such as tweet2vec, image2vec, and mol2vec. Each entry typically includes links to the original research paper and available code implementations in languages like Python, Java, and C++. It's an invaluable resource for anyone working with embeddings in natural language processing, graph analysis, and other machine learning domains, facilitating the discovery of relevant models and their implementations.

awesome-adversarial-machine-learning is a curated list of resources focused on adversarial machine learning, hosted on GitHub. It serves as a valuable starting point for individuals interested in this specialized area of AI. The repository organizes information into categories such as blogs, academic papers, and talks, covering topics like general adversarial examples, attacks on image classification, reinforcement learning, and speech recognition, as well as defense mechanisms. While the maintainer notes that the list is no longer updated with the latest papers, it remains a strong reference for foundational knowledge in adversarial machine learning. This open-source project is ideal for researchers and students looking to explore the field.

awesome-claude-skills is a comprehensive, curated list of Claude Skills, resources, and tools designed to customize and enhance Claude AI workflows, with a particular focus on Claude Code. Claude Skills are specialized folders containing instructions, scripts, and resources that Claude dynamically discovers and loads when relevant to tasks. This open-source GitHub repository details how Skills work, their progressive disclosure architecture for efficiency, and provides guides for getting started via the Claude.ai web interface, Claude Code CLI, or Claude API. It features official skills for document processing (docx, pdf, pptx, xlsx), design (algorithmic-art, canvas-design), development (frontend-design, web-artifacts-builder), communication, and skill creation. The repository also highlights community-contributed skills, tools for skill creation, best practices, and security guidelines, emphasizing the importance of vetting skills due to arbitrary code execution capabilities.

awesome-llm-books offers a meticulously curated list of books specifically focused on Large Language Models (LLMs), designed for engineers and developers. The list is compiled through a rigorous process including reviewing blurbs, tables of contents, star ratings, and social media discussions to ensure relevance and quality. Each book entry provides details such as authors, publisher, publication year, and star ratings from Amazon and Goodreads, along with direct links to purchase or learn more. This resource aims to simplify the discovery of high-quality educational materials for those looking to deepen their understanding and practical skills in LLM development.

Awesome-LLM-KG is a comprehensive collection of academic papers and resources dedicated to the integration of Large Language Models (LLMs) and Knowledge Graphs (KGs). This repository aims to provide researchers and practitioners with a clear roadmap and understanding of how to leverage the strengths of both LLMs, known for their generalizability, and KGs, valued for their structured factual knowledge. It categorizes research into three main frameworks: KG-enhanced LLMs, LLM-augmented KGs, and Synergized LLMs + KGs, detailing involved techniques and applications. The project is actively updated with new research, including recent papers accepted at major conferences like ICML, NeurIPS, and ACL, making it a valuable resource for staying current in the field.

Awesome-LLMs-for-Video-Understanding is a comprehensive, open-source GitHub repository dedicated to the rapidly evolving field of video understanding using Large Language Models (Vid-LLMs). It serves as a vital resource for researchers, academics, and engineers by curating the latest papers, associated code, and relevant datasets. The repository features a detailed survey on Vid-LLMs, covering various techniques, training strategies, tasks, datasets, benchmarks, and evaluation methods. It also introduces novel taxonomies for Vid-LLMs based on video representation and LLM functionality, making it easier to navigate the complex landscape of this domain. Regular updates ensure the content remains current, including new models, benchmarks, and redesigned figures and tables for clarity.

awesome-ml-courses offers a comprehensive, curated list of free machine learning and artificial intelligence courses, all featuring high-quality video lectures from renowned AI researchers and educators. This resource goes beyond just videos, linking to course websites that provide detailed lecture notes, supplementary readings, and practical assignments. It caters to both beginners, with introductory lectures requiring some knowledge of linear algebra, calculus, and probability, and advanced learners, offering courses that delve into specialized topics like deep unsupervised learning, graph neural networks, and advanced reinforcement learning. The platform serves as an excellent starting point for anyone looking to deepen their understanding of AI and ML concepts.

Basic-Mathematics-for-Machine-Learning is an open-source GitHub repository designed to help individuals overcome the mathematical challenges associated with Machine Learning, Deep Learning, and other AI fields. The repository provides foundational knowledge in key mathematical areas such as Algebra, Calculus, Statistics, and Probability. It includes practical code examples, primarily in Python notebooks, demonstrating the application of these concepts using essential libraries like NumPy, Pandas, and Matplotlib. The resource emphasizes the importance of mathematics for selecting algorithms, choosing parameter settings, understanding bias-variance tradeoffs, and estimating confidence intervals. It covers topics like Linear Algebra, Probability Theory, Statistics, Multivariate Calculus, and Algorithms, making it a comprehensive guide for those looking to strengthen their mathematical background for AI.

MiniGPT-4 is an open-source initiative dedicated to advancing vision-language understanding by integrating advanced large language models. The project offers open-sourced code for both MiniGPT-4 and its successor, MiniGPT-v2, enabling researchers and developers to explore and build upon state-of-the-art vision-language capabilities. It functions as a unified interface, facilitating multi-task learning across various vision and language domains. The project provides detailed instructions for installation, preparation of pretrained LLM weights (including Llama2 Chat and Vicuna), and model checkpoints. Users can launch local demos for both MiniGPT-v2 and MiniGPT-4, with options to optimize GPU memory usage. Training and finetuning details are also provided, making it a comprehensive resource for those working with vision-language models.

ML-For-Beginners is a comprehensive, open-source curriculum developed by Microsoft Cloud Advocates, designed to introduce individuals to classic machine learning concepts over 12 weeks. The curriculum comprises 26 lessons and 52 quizzes, focusing on practical, project-based learning using primarily Scikit-learn, while intentionally avoiding deep learning topics covered in their AI for Beginners curriculum. Each lesson includes pre- and post-lesson quizzes, written instructions, solutions, assignments, and challenges, ensuring a hands-on approach to skill development. The content is available in over 50 languages and includes resources for both students and teachers, with video walkthroughs and a troubleshooting guide. It emphasizes a pedagogical approach that combines project-based learning with frequent assessments to enhance concept retention.

Malakah is an AI-powered legal platform specifically designed for Saudi law, offering a comprehensive suite of tools to streamline legal operations. It provides instant legal solutions, effortless contract automation, and ensures 100% compliance with Saudi law, available in both Arabic and English. Key features include an AI Legal Assistant for rapid insights and drafting, secure e-signature solutions with audit trails, and document comparison workflows for tracking revisions. Malakah also offers seamless document translation, a legal library with current Saudi laws and regulations, and playbooks for process optimization. The platform emphasizes total privacy and secure handling of data, aligning with Saudi-compliant security standards, and provides fresh, reliable data to ensure accuracy.

Melting Pot is an open-source suite of test scenarios specifically designed for multi-agent reinforcement learning (MARL). Developed by Google DeepMind, it offers researchers a robust platform to train and evaluate AI agents in complex social situations. The tool includes over 50 multi-agent games (substrates) and more than 256 unique test scenarios, allowing for the assessment of generalization to novel social interactions like cooperation, competition, and trust. It is built on DeepMind Lab2D and provides tools for interactive play, evaluation of trained models, and example training scripts using frameworks like RLlib. Melting Pot aims to become a standard benchmark for MARL research, with ongoing development to expand its coverage of social interactions and generalization scenarios.

Bilby is an AI operating system specifically designed for government entities, offering advanced software for regulation, compliance, and prediction. It leverages custom AI models, knowledge graphs, and multilingual processing to convert complex government activity into hierarchical, clean data and actionable software. The platform has already processed over 75 million artifacts from 130,000 decision-makers across more than 40 countries, creating predictive insights. Bilby aims to improve how the world is governed by providing solutions that offer significant improvements over traditional methods, especially in regions like the Middle East and Asia. Its expert-led innovation, proprietary technology, and global reach make it a comprehensive intelligence solution for government agencies and financial services.

Black Girl Ai is dedicated to driving diversity and innovation in technology by empowering and inspiring Black and Brown girls aged 8-12 in the field of Artificial Intelligence. The platform offers fun, hands-on skills training and builds a vibrant community where young innovators can collaborate on exciting projects and celebrate their unique voices. It champions diversity by ensuring Black and Brown girls are included, valued, and represented in shaping the future of tech. Black Girl Ai also provides an enhanced Storybook app for younger girls (1-10) to inspire curiosity about AI and technology. The initiative, founded by Ayana Elon, aims to make a lasting impact by encouraging young girls to explore AI, break barriers, and contribute their unique perspectives to the tech world.

MIRIX is a multi-agent personal assistant that intelligently tracks on-screen activities and answers user questions. It captures real-time visual data and consolidates it into structured memories, transforming raw inputs into a rich knowledge base that adapts to your digital experiences. The system features six specialized memory components (Core, Episodic, Semantic, Procedural, Resource, Knowledge Vault) managed by dedicated agents. It boasts a privacy-first design, storing all long-term data locally with user-controlled settings, and offers advanced search capabilities with PostgreSQL-native BM25 full-text search and vector similarity support. MIRIX also supports multi-modal input, seamlessly processing text, images, voice, and screen captures.