Research & Education

The machine-learning-list is a curated curriculum hosted on GitHub, designed to educate individuals on foundation models, with a particular emphasis on language models. It offers a balanced approach, covering both techniques relevant for deploying machine learning in production and strategies for long-term scalability. The curriculum is structured into tiers, allowing users to progress from fundamental concepts like neural networks and gradient descent to advanced topics such as world models, causality, and AI safety. Key areas include Transformers, various foundation model architectures (GPT-2, GPT-3, LLaMA), training and finetuning methods, reasoning and runtime strategies (Chain of Thought, Tree of Thoughts), and applications in science, forecasting, and search. It also delves into practical aspects like production deployment, benchmarks, and datasets, making it a valuable resource for anyone looking to deepen their understanding of modern AI.

Deep_learning_cookbook is an open-source repository featuring 35 Python notebooks that illustrate fundamental machine learning techniques using the Keras framework. These notebooks are designed to accompany the book "Deep Learning Cookbook" but are fully functional as standalone educational resources. The collection covers a wide range of topics, from using pre-trained word embeddings and building recommender systems to generating text, classifying sentiments, and working with image recognition networks. It also includes examples for productionizing embeddings and preparing Keras models for deployment on platforms like TensorFlow Serving and iOS. While a GPU is not strictly required, its use is recommended for faster processing.

deeplearning-notes offers a detailed collection of study materials for the Deep Learning Specialization courses by deeplearning.ai on Coursera, led by Andrew Ng. This resource is designed to help students and professionals understand the core principles of deep learning, including neural networks, convolutional networks (CNNs), recurrent neural networks (RNNs), and sequence models. It covers practical aspects like hyperparameter tuning, optimization algorithms, and structuring machine learning projects. The notes delve into various topics such as Adam, Dropout, BatchNorm, Xavier/He initialization, and provide insights into real-world applications in healthcare, autonomous driving, and natural language processing. The content is structured to support learning in Python and TensorFlow, making it a valuable companion for those mastering deep learning theory and its industrial application.

deeplearning4nlp-tutorial offers a hands-on tutorial for deep learning, specifically tailored for Natural Language Processing (NLP). This GitHub repository provides comprehensive resources, including slides and source code, from various lectures and seminars. Users can explore different deep learning models such as Feed Forward Architectures for sequence classification, Convolutional Neural Networks for sentence/text classification and relation extraction, and Long-Short-Term-Memory (LSTM) Networks for sequence classification. The tutorial supports Python 2.7 or 3.6, Keras, and either Theano or TensorFlow as backend, making it a valuable resource for students and practitioners looking to implement deep learning methods in NLP.

NLPBook is a comprehensive open-source resource focused on neural networks and large language models within the field of Natural Language Processing. Authored by Tong Xiao and Jingbo Zhu, this book is designed for anyone interested in NLP and deep learning. It integrates content from previously published articles with significant new material, covering foundational concepts in machine learning and neural networks, basic NLP models like word vectors, recurrent and convolutional sequence models, and transformers. The book also delves into advanced topics such as pre-training, generative models, prompting, alignment, and inference for large language models. All chapters are available in PDF format, and the book has been translated into multiple languages using LLMs.

SlowMo AI is an advanced AI-powered educational platform specifically designed for children aged 6-12. It provides a safe and engaging environment for young learners to explore the world of artificial intelligence through interactive games, AI literacy modules, and prompt engineering challenges. The platform aims to foster critical thinking skills and introduce fundamental AI concepts in an age-appropriate manner. With a focus on educational safety, SlowMo AI ensures content is filtered and suitable for its target audience, making learning about AI both fun and secure. It helps children understand how AI works and how to interact with it responsibly, preparing them for a future increasingly shaped by technology.

SaaSGuru is a comprehensive online platform designed to supercharge Salesforce careers. It provides a robust learning experience through training bootcamps, self-paced certification exam prep courses, and hands-on labs for real-world project experience. Learners benefit from live classes led by global Trailblazers, 1-1 mentoring, interview practice, and job readiness support. The platform boasts a 97% pass rate for certifications and offers unlimited doubt-clearing sessions, 24x7 community support, and a Gen-AI-powered Personal Learning Assistant. With over 150,000 learners across 60+ countries, SaaSGuru aims to help individuals build a strong foundation in Salesforce, achieve certifications, and secure their dream jobs.

Generative AI on AWS is an open-source resource developed by O'Reilly Media, offering a detailed guide to building and deploying generative AI applications on the Amazon Web Services platform. The resource is structured into chapters covering essential topics such as generative AI use cases, prompt engineering, large language models, and fine-tuning techniques like PEFT and RLHF. It also delves into practical aspects like quantization, distributed computing, and deploying generative AI applications, including Retrieval Augmented Generation (RAG) and multimodal models. The content is designed to help users leverage AWS services, including Amazon Bedrock, for their generative AI projects.

GenerativeAICourse offers a comprehensive and practical course designed to teach generative AI from first principles. It covers the invention of AI, the rise of large language models (LLMs), and moves into hands-on tutorials. Users will learn to build basic chatbots, implement Retrieval-Augmented Generation (RAG), create AI agents, explore Model Context Protocol (MCP), and understand the requirements for building production-grade AI applications. The course emphasizes practical relevance, avoiding jargon and overly academic content, making AI engineering accessible for those looking to build real-world AI solutions that scale. It highlights key differences between traditional ML and AI engineering, focusing on adapting models rather than just training them.

Brisk Teaching is an AI-powered education platform designed to assist teachers and students by integrating AI capabilities directly into existing workflows. It enables educators to quickly generate instructional materials like lesson plans, quizzes, and presentations from various resources such as Google Docs, YouTube videos, or PDFs. The platform also offers robust feedback tools, including targeted, Glow & Grow, and rubric-aligned feedback, which can be applied to individual assignments or entire classes. Additionally, Brisk Teaching features an "Inspect Writing" tool to help teachers understand student writing processes and identify potential AI-generated content, promoting academic integrity. For students, Brisk Boost provides a teacher-controlled AI workspace for personalized, interactive learning experiences.

GenAI_LLM_timeline is a comprehensive GitHub repository dedicated to chronicling the evolution of Generative AI and Large Language Models (LLMs). It meticulously organizes significant events, product launches, research papers, and news, both preceding and following the announcement of ChatGPT. The repository acts as a valuable historical scene, curating information to offer a clear timeline of advancements in LLMs and Generative AI. It's an open-source resource, making it accessible for anyone interested in tracking the rapid development and milestones within this dynamic field.

Generative-ai is a GitHub repository offering a comprehensive collection of resources for learning and mastering Generative AI. It features a detailed roadmap for GenAI and AI/ML, interactive learning through AI-ML Companion with animated diagrams, quizzes, and hands-on Python exercises. The repository covers essential GenAI terms, LLM fundamentals, vector embeddings, prompt engineering, and AI design patterns. It also delves into architecture and technical stacks, including GenAI tech stacks, LLM providers, and advanced RAG decision flows. Practical use cases and projects are provided, spanning Retrieval-Augmented Generation (RAG), Agentic AI, conversational AI, and data analytics applications. Additionally, it offers career and interview preparation guides for various AI roles.

Roadmap-To-Learn-Agentic-AI is an open-source GitHub repository offering a comprehensive guide to mastering agentic AI systems. It begins with foundational knowledge in Python programming and essential machine learning concepts, including Natural Language Processing (NLP) techniques like TFIDF and Word2vec. The roadmap then progresses to in-depth Deep Learning for NLP, transformer explanations, and extensive Generative AI tutorials with end-to-end projects. A significant portion is dedicated to Agentic AI tutorials, exploring various frameworks such as Langchain, LangGraph, Agno, Phidata, CrewAI, and Autogen. This resource is ideal for individuals looking to build a strong understanding and practical skills in the rapidly evolving field of agentic AI.

info8010-deep-learning is a GitHub repository offering a comprehensive set of lecture materials for the INFO8010 Deep Learning course at ULiège. This resource includes lecture PDFs, associated code examples (e.g., for polynomial regression, multi-layer perceptrons, automatic differentiation in PyTorch, convolutional neural networks, attention, transformers, GPT, graph neural networks, uncertainty, auto-encoders, and diffusion models), and homework assignments designed to familiarize users with the PyTorch library. It also provides a course syllabus, project guidelines, and archived lectures from previous editions, making it an invaluable educational tool for anyone looking to learn or teach deep learning concepts.

LLMRiddles is an open-source project that provides a game-like environment for users to explore and understand prompt engineering. Players are challenged to craft questions that interact with various language models (like ChatGPT, ChatGLM, DeepSeek, and Mistral-7B) to achieve specific, required outputs. This platform aims to deepen participants' understanding of how to cleverly construct prompts and trigger surprising responses from AI systems, highlighting the power of deep learning and natural language processing. It offers online versions for direct access and local deployment options, supporting multiple LLMs and languages. Users can also contribute custom levels to the game.

LLMsPracticalGuide is a comprehensive, actively updated resource offering a curated list of practical guides for Large Language Models (LLMs). It is based on a survey paper titled "Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond" and includes an evolutionary tree of modern LLMs. The guide aims to assist practitioners in understanding and applying LLMs in natural language processing (NLP) applications, covering various aspects such as model types (BERT-style, GPT-style), data considerations (pretraining, finetuning, test data), NLP tasks (NLU, generation, knowledge-intensive), and practical concerns like efficiency, trustworthiness, and alignment. It also details usage restrictions based on model and data licensing information, making it a valuable resource for both research and practical implementation.

The UKRI AI Centre for Doctoral Training (CDT) in Designing Responsible Natural Language Processing, based at the University of Edinburgh, focuses on training future researchers and innovators. The program emphasizes the development of responsible and trustworthy natural language processing systems. Studentships are available for PhD candidates, with applications opening for September 2026. The CDT covers five core skills domains, including responsible NLP data and models, explainable NLP, human-NLP partnership design, NLP governance and accountability, and co-creation for NLP futures. The center collaborates with a diverse community of partners from industry, advocacy groups, and policy-making organizations, offering students project challenges, placements, and internships.

Gooru Learning offers MyGooru AI for Personalized Pathways (MAP), an AI-driven personalization infrastructure designed to deliver assured outcomes across various industries including learning, finance, health, and enterprise. MAP goes beyond generative AI by using formal reasoning to build beliefs about each user and generate mathematically certain pathways, ensuring engagement and completion. It actively senses user mindsets, motivation, confidence, and intent, continuously updating probabilistic beliefs across knowledge, mindsets, interests, abilities, and community. This approach helps lower customer acquisition costs through personalized discovery and smarter conversion funnels, while increasing lifetime value via adaptive engagement and outcome completion. Gooru also provides tools for instructors, institution leaders, and curriculum developers.

Machine-Learning-Tutorials is an open-source GitHub repository offering a comprehensive, topic-wise curated list of machine learning and deep learning tutorials, articles, and other educational resources. It covers a wide range of subjects from foundational concepts like linear and logistic regression, model validation, and statistics, to advanced topics such as deep learning frameworks, natural language processing, computer vision, and reinforcement learning. The repository includes links to university courses, useful blogs, interview resources, and cheat sheets, making it a valuable hub for anyone looking to learn or deepen their understanding of AI and ML. It also features curated lists of R and Python tutorials specifically for data science, NLP, and machine learning.

Machine-Learning-Guide is an extensive open-source resource designed to help individuals learn about and improve their efficiency in machine learning development. It offers a wealth of information on various machine learning tools, libraries, frameworks, and large language models (LLMs). The guide includes learning resources, developer resources, courses, certifications, books, and YouTube tutorials. It also details specific ML/Deep Learning frameworks like TensorFlow, PyTorch, and Keras, along with tools for deploying and running LLMs. This resource is ideal for anyone looking to deepen their understanding of machine learning applications and development practices.

ML-YouTube-Courses is a curated repository maintained by DAIR.AI, dedicated to promoting open AI education by indexing and organizing a wide range of machine learning and AI courses found on YouTube. The collection spans various topics including Machine Learning fundamentals, Deep Learning, Foundation Models, LLMOps, Natural Language Processing, Computer Vision, Reinforcement Learning, and Graph Machine Learning. Each course entry typically includes a brief description of its content and a direct link to the YouTube playlist or course materials. This resource is ideal for students, researchers, and practitioners looking to enhance their knowledge and skills in AI through structured, free online learning.

MLE-Flashcards provides a comprehensive collection of over 250 detailed flashcards designed for individuals looking to review and solidify their understanding of machine learning, computer vision, and computer science. Developed from years of ML research, coursework, and independent study, these flashcards are an excellent resource for both academic study and interview preparation. Topics span a wide range, including computer science fundamentals, classical machine learning, modern deep learning, 2D/3D computer vision, natural language processing (NLP), reinforcement learning, generative models, and large language models (LLMs). While assuming a foundational understanding of these subjects, the flashcards can also offer a broad overview for newcomers, suggesting supplementary educational materials. The content is regularly updated, with recent additions covering RL, NeRFs, Gaussian splatting, and VLMs, ensuring relevance in a rapidly evolving field.

ML-Notebooks is an open-source repository offering a collection of machine learning notebooks tailored for diverse tasks and applications. The notebooks prioritize minimalism, reusability, and extensibility, making them ideal for educational and research endeavors. Users can leverage these resources to explore concepts like computational graphs, PyTorch fundamentals, counterfactual explanations, and various neural network implementations from scratch. The repository also includes specialized notebooks for NLP tasks such as text classification and data augmentation, Transformer models for text classification and machine translation, and Computer Vision applications including Siamese Networks and GANs. It supports Codespaces for easy environment setup with all dependencies pre-installed, streamlining the learning and development process.

Limbiks is an AI-powered study tool designed to streamline the learning process by generating comprehensive study materials from diverse sources. Users can upload PDFs, presentations, notes, images, or even input YouTube video links and Wikipedia articles to instantly create flashcards, multiple-choice questions, and personalized study guides. The platform supports 21 languages and includes advanced study features such as spaced repetition, AI image occlusion, and hints/explanations. Limbiks also offers seamless integration with popular flashcard tools like Anki, Quizlet, and Cram, allowing users to download or sync their generated content for flexible study routines. It's an ideal solution for students looking to optimize their study time and master complex subjects efficiently.