Research & Education

python-ml-course is an open-source educational resource designed to introduce individuals to Machine Learning using Python. The comprehensive course covers a wide range of topics, from basic Python installation and data preprocessing to advanced concepts like Deep Learning and Reinforcement Learning. It includes practical exercises, real-world datasets, and all source code on GitHub, making it suitable for hands-on learning. The course is taught by Juan Gabriel Gomila, a professional in Data Science, and aims to make complex mathematical theories and algorithms accessible. It caters to students, programmers, and data analysts looking to specialize or enhance their skills in the lucrative field of Data Science.

Practical-Deep-Learning-for-Coders-2.0 offers a comprehensive collection of notebooks designed for the "A walk with fastai2" Study Group and Lecture Series. This resource is ideal for individuals looking to delve into practical deep learning, covering key areas such as computer vision, tabular neural networks, and natural language processing. The course, which includes live-streamed lectures and project work, provides a structured learning path for undergraduates and others interested in the fastai framework. While the notebooks are now hosted on a new GitHub repository, this original repository serves as a valuable archive of the course material, offering insights into various deep learning applications and techniques.

rag-tutorial-v2 is an open-source tutorial designed to guide users through the process of building Retrieval Augmented Generation (RAG) systems. This improved version (v2) focuses on practical implementation, incorporating local LLMs for enhanced privacy and control, and demonstrating effective database update strategies. The tutorial also emphasizes robust testing methodologies to ensure the reliability and performance of the RAG system. It's a valuable resource for developers and researchers looking to understand and implement advanced RAG techniques, offering a hands-on approach to integrating LLMs with external knowledge bases.

Raise Labs is an AI-driven platform dedicated to transforming education and fostering personal and organizational growth. It creates "growth spaces" powered by AI and designed for people, aiming to shift individuals and organizations from performance to purpose, and from pressure to flow. Key offerings include TeachingHero, an AI-powered platform for personalized learning environments, and OteraX, a learning and evidence platform for mandatory regulatory training that is online, asynchronous, and audit-ready. Raise Labs also provides custom software development, content migration services to AI-personalized formats, and EdTech consulting. The platform emphasizes consciousness and connection in learning, supporting educators, companies, coaches, and founders in building meaningful learning cultures.

Anthropic's Interactive Prompt Engineering Tutorial offers a comprehensive, step-by-step guide to mastering prompt engineering for Claude. This course is designed to help users understand the basic structure of effective prompts, recognize common failure modes, and apply '80/20' techniques to address them. It delves into Claude's strengths and weaknesses, enabling users to build robust prompts from scratch for various use cases. The tutorial is structured into 9 chapters with accompanying exercises, allowing for hands-on practice. Each lesson includes an "Example Playground" for experimentation and an answer key for self-assessment. While it uses Claude 3 Haiku, it acknowledges the existence of more intelligent models like Claude 3 Sonnet and Opus. A Google Sheets version with Anthropic's Claude for Sheets extension is also available for a more user-friendly experience.

CoitHub positions itself as the primary entry point and router for decentralized intelligence and private LLM meshes. The platform aims to facilitate the connection and management of distributed AI systems, emphasizing privacy and decentralized control. While specific features are not detailed on the public-facing pages, its core offering revolves around enabling users to interact with and manage intelligent agents within a decentralized framework. This suggests a focus on secure and private AI operations, potentially catering to users who prioritize data sovereignty and distributed computing for their AI needs.

Jazzberry is a platform designed for building and experimenting with reinforcement learning (RL) environments. It offers tools and resources tailored for researchers and students in the field of artificial intelligence. The platform supports a variety of RL algorithms, enabling users to explore and implement different approaches to model training. A key feature is the ability to customize environments for specific tasks, providing flexibility for diverse research needs. Jazzberry aims to simplify the complex process of developing and testing RL agents, making advanced AI research more accessible and efficient for its users.

TEACH UP is an AI-powered platform specializing in Adaptive Training®, designed to revolutionize corporate learning and development. It offers an Adaptive Training® Studio, an AI-assisted authoring tool (LXP) for creating innovative training experiences, and an Adaptive Training® Platform, an AI-augmented LCMS for comprehensive course creation, distribution, and tracking. Key features include intuitive content creation with an AI Assistant, personalized learning paths that adapt in real-time, and AI-generated feedback for coached training scenarios. The platform also provides optimized monitoring of engagement and performance, and flexible content distribution. It aims to make training 30 times faster to create and 4.1 times more effective than traditional e-learning, ensuring 100% mastery through adaptive learning principles.

D-ID Creative Reality Studio is an all-in-one platform for creating cutting-edge AI videos featuring digital humans. It leverages D-ID’s deep-learning face animation technology, LLM text generation, and text-to-image capabilities to bring content to life. Users can select from pre-made avatars, upload their own facial images, or generate portraits using Stable Diffusion. The studio supports various visual elements like backgrounds, videos, and texts, organized in layers, and allows for customization of avatar expressions and voice, including voice cloning for enterprise users. Videos are generated in MP4 format, with resolutions up to 1080p and lengths up to 5 minutes, making it suitable for a wide range of commercial and creative purposes.

PuzzleGenio is a comprehensive online puzzle maker offering a variety of puzzle types including crosswords, Sudoku, word searches, jigsaw puzzles, nonograms, and bingo cards. Users can create custom puzzles with AI-powered word and clue generation, adjust difficulty levels, and play online directly in their browser. The platform supports instant sharing via links and provides high-quality printable PDF downloads, making it ideal for educators, parents, event organizers, and content creators. It also offers SVG and DXF export options for craft enthusiasts. PuzzleGenio is 100% free for everyday use, with optional Pro and Education plans for commercial use, ad-free experience, and enhanced export quality.

Course-it is an AI-powered tool designed to assist course creators in developing well-structured and effective educational content. It acts as a thinking partner, helping users validate their course ideas and transform them into strategic outlines. The platform supports the creation of detailed course modules, facilitates the development of engaging quizzes, and aids in building a comprehensive roadmap for the entire course. By leveraging AI, Course-it aims to streamline the course creation process, making it more efficient for educators and content developers to bring their ideas to fruition.

ExamHUB is an award-winning, globally available online examination platform designed to make learning engaging and effective for students from Grade 6 to 13. Aligned with Edexcel and Cambridge syllabuses, it offers a vast question bank of over 150,000 exam questions. The platform incorporates interactive quizzes, leaderboards, and AI-driven learning tools to revolutionize exam preparation through gamified experiences. Students can download the app, access questions, track progress, and utilize AI insights to master their subjects. Educators can also leverage ExamHUB to create engaging learning environments.

Computer-vision-in-action is a comprehensive, open-source learning platform designed for individuals interested in mastering computer vision. It offers a closed-loop learning environment where users can interactively run code directly online, eliminating the need for complex local setup. The platform features an electronic book, available in both Chinese and English, covering fundamental theories, practical applications, and advanced topics like Transformer models and generative adversarial networks. It includes detailed project guidance, code implementations, and a community forum for reader interaction and support. The platform emphasizes a 'learn by doing' approach, allowing users to modify code and observe results in real-time.

all-in-rag is an open-source educational resource designed for developers interested in Retrieval-Augmented Generation (RAG) technology. It offers a full-stack guide, covering RAG core concepts, data processing workflows, index building and optimization, advanced retrieval techniques, and system evaluation. The resource emphasizes hands-on practice with rich project examples, including multi-modal RAG support for text and image retrieval. It aims to provide a systematic learning path for building production-ready intelligent Q&A and knowledge retrieval systems, addressing the fragmented nature of existing RAG tutorials. The project is suitable for developers with Python programming skills and an interest in AI engineering.

AlphaTree-graphic-deep-neural-network is an open-source project offering a comprehensive AI roadmap for machine learning, deep learning, GANs, GNNs, NLP, and big data. It aims to guide users from novices to qualified engineers by providing a structured learning path, abundant source code in Python and PyTorch, and detailed explanations of fundamental concepts. The resource includes deep learning papers with official TensorFlow and Caffe source code, along with applications in recommendation algorithms and knowledge graphs. It's designed to help individuals quickly grasp cutting-edge techniques, prepare for interviews, and understand the practical application of AI in various engineering projects.

deep-learning-keras-tf-tutorial is an open-source project offering a comprehensive tutorial series for learning deep learning. It focuses on practical implementation using TensorFlow 2.0, Keras, and Python, making it suitable for beginners. The series covers a wide range of topics from fundamental concepts like activation functions and gradient descent to more advanced areas such as CNNs, transfer learning, word embeddings, and distributed training. Each topic is accompanied by code examples, allowing users to learn deep learning from scratch and build a solid foundation in the field.

DeepLearningTutorial offers a comprehensive deep learning tutorial translated into Chinese from the DeepLearning 0.1 documentation. This resource is designed for individuals looking to understand and implement deep learning algorithms and models. All examples within the tutorial are coded using Python and Theano, a powerful third-party library that enables the use of GPUs or CPUs for running Python code. The tutorial covers various topics, including getting started with deep learning, classifying MNIST digits using logistic regression, multilayer perceptrons, convolutional neural networks (LeNet), denoising autoencoders, stacked denoising autoencoders, and restricted Boltzmann machines. It serves as an excellent educational resource for Chinese-speaking students and researchers interested in the field of deep learning.

llm_note is an extensive collection of notes and resources designed for individuals looking to deepen their understanding of large language models (LLMs). It covers fundamental aspects such as LLM inference, the intricate structure of transformer models, and detailed code analysis of various LLM frameworks. Additionally, the resource delves into high-performance computing (HPC) topics, offering insights into Triton and CUDA programming for optimizing LLM operations. The project also features a self-made large model inference framework, built with Triton and PyTorch, emphasizing lightweight design and ease of use. This framework aims to simplify GPU kernel development by leveraging PyTorch-like syntax for Triton operators, bypassing the complexities of direct CUDA programming. It includes support for advanced features like FlashAttention and PageAttention, and demonstrates significant speed improvements over standard libraries for certain LLM models.

LLMForEverybody is a comprehensive resource designed to make large language model (LLM) knowledge accessible to everyone. It features a curated database of LLM interview questions, covering topics from foundational concepts to advanced applications, ideal for job seekers preparing for spring/autumn recruitment drives. The platform also offers a systematic approach to studying LLM research papers, starting from the 2017 Transformer paper and progressing through key technological advancements. Complementing these resources are continuously updated video tutorials available on Bilibili and YouTube, ensuring a multi-modal learning experience. The goal is to equip users with the knowledge to confidently discuss LLMs with interviewers and advance their careers.

Learn Place Verified Experience provides an AI-powered virtual internship program designed for students, recent graduates, and career changers. The platform offers real-world projects with AI assistance, allowing users to build a professional portfolio that employers can verify. Key features include verified skills tied to completed milestones, a rigorous review process with detailed AI feedback, and video final reviews where interns explain their work. The program aims to bridge the gap between academic qualifications and the practical experience required for entry-level jobs, offering an interview guarantee and support for career advancement. It also includes anti-cheating verification and tamper-proof records to ensure the authenticity of the experience.

ml-basics is an open-source repository hosted on GitHub, offering a collection of exercise notebooks specifically designed for Machine Learning modules available on Microsoft Learn. This resource provides practical, hands-on experience for individuals looking to understand and apply fundamental machine learning concepts. The repository includes notebooks covering various topics such as data exploration, regression, classification, clustering, and deep neural networks using both PyTorch and TensorFlow. It serves as a valuable supplementary tool for students and learners engaging with Microsoft's official machine learning curriculum, allowing them to practice coding and reinforce their theoretical knowledge with real-world examples.

MyLessonPal is an AI-powered lesson planning tool designed specifically for teachers, aiming to streamline the creation of educational content. It delivers standards-aligned teaching resources directly to the user's inbox every morning, significantly reducing the time spent on lesson preparation. The platform helps educators save over 12 hours a week by acting as an AI teaching assistant. While the live content doesn't detail specific features beyond 'standards-aligned teaching resources,' the core value proposition is clear: efficient and automated lesson plan generation to support teachers in their daily tasks. This tool is ideal for educators looking to enhance productivity and ensure their teaching materials meet required standards with minimal effort.

T81-558 is a comprehensive GitHub repository containing teaching materials for the T81-558: Keras - Applications of Deep Neural Networks course offered at Washington University in St. Louis. This resource focuses on the Keras/TensorFlow version of the curriculum, covering a wide array of deep learning topics. Students and enthusiasts can explore modules on Python preliminaries, Pandas for machine learning, TensorFlow and Keras fundamentals, training for tabular data, regularization, CNNs for vision, Generative Adversarial Networks (GANs), Kaggle competitions, transfer learning, time series analysis, reinforcement learning, and deploying models with Flask. The repository includes Jupyter notebooks for practical application and a complete textbook available on GitHub, making it an invaluable resource for learning and applying deep neural network concepts.

Teaching-material is a comprehensive open-source repository designed to provide preparatory materials for machine learning and deep learning courses. Developed for use at prestigious institutions like Stanford and Cornell, it focuses on foundational skills in Python and Numpy. The repository includes tutorials essential for students embarking on advanced machine learning studies, covering topics relevant to probabilistic graphical models, deep learning, applied machine learning, and deep generative models. It offers an iPython notebook for interactive learning, which can be followed directly on GitHub or executed locally, making it a flexible resource for both self-study and structured academic environments.