Research & Education

DeepLearningProject offers an extensive machine learning tutorial designed to guide users through an entire machine learning pipeline from the ground up. Unlike typical short tutorials, this project focuses on a full pipeline, covering all implementation decisions and details required for real-world machine learning applications. It moves beyond standard datasets like MNIST or CIFAR, encouraging users to create their own datasets. The tutorial progresses from conventional machine learning algorithms to deep learning, providing a holistic learning experience. Originally developed as a class project for Harvard University, it has been updated to include a PyTorch version. The project emphasizes practical setup with conda environments and Docker containers, addressing common installation issues and bugs.

DL-workshop-series is an open-source GitHub repository maintained by Machine Learning Tokyo (MLT), offering comprehensive materials for deep learning workshops. It features practical implementations and theoretical insights into convolution operations and learning processes within deep neural networks. The repository includes Colab notebooks with examples of kernel applications and functions for constructing Keras models, covering architectures like AlexNet, VGG, Inception, MobileNet, ResNet, and YOLO. It serves as a valuable resource for individuals and groups looking to learn and practice deep learning techniques, providing both code and presentation slides for a structured learning experience.

dl_tutorials is an open-source GitHub repository offering a comprehensive set of deep learning tutorials, structured into weekly modules. It guides users through fundamental concepts such as Python basics, logistic regression, and optimization methods, progressing to advanced topics like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their applications in image detection, semantic segmentation, and handwriting generation. The tutorials include practical exercises, such as implementing MLPs and CNNs on custom datasets, and cover modern architectures like AlexNet, GoogLeNet, and Residual Networks. It also delves into advanced concepts like deep reinforcement learning, adversarial attacks, and generative adversarial networks, making it a valuable resource for those looking to understand and implement deep learning techniques.

fastai_deeplearn_part1 is an open-source repository offering comprehensive notes and resources for the fast.ai deep learning course. It serves as a valuable educational aid, providing structured outlines for different versions of the deep learning and machine learning courses, ranging from Fall 2016 to Spring 2020. The repository includes helpful resources such as a directory of fastai and deep learning terms, solutions for common errors, FAQs for beginners, and best practices. Additionally, it features technical tools and tips for working with platforms like AWS, Kaggle CLI, and Jupyter Notebooks, making it a practical guide for students and developers engaging with deep learning concepts. The content is primarily in Markdown format, making it easily accessible and reviewable.

DistilBook is an AI-powered learning platform designed to convert any document, particularly textbooks, into comprehensive video courses. The platform's AI analyzes the content and generates animated whiteboard-style videos that explain every concept, ensuring no information is lost from the original source. This approach aims to eliminate passive reading by providing structured video lessons with instant doubt clarification. Users can upload PDFs, and the AI tutor offers contextual answers to questions based on the textbook material. DistilBook offers a growing library of courses across various subjects, all accessible for free, making it suitable for students, professionals, and lifelong learners seeking efficient mastery of complex topics.

get-started-with-JAX is a comprehensive repository designed to simplify the learning curve for JAX, Flax, and Haiku, which are increasingly popular alternatives to PyTorch and TensorFlow in the machine learning landscape. This resource offers a series of "Machine Learning with JAX" tutorials, presented through both YouTube videos and accompanying Jupyter Notebooks. The content is curated based on what the creator found most useful during their own learning journey, ensuring practical and relevant information. It covers fundamental concepts like `jit`, `grad`, and `vmap`, progresses to training neural networks, and delves into frameworks like Flax, with Haiku tutorials planned. The repository also provides links to other valuable JAX resources, including videos and blogs, making it an all-encompassing guide for those looking to dive into the JAX ecosystem.

java-go-python is an open-source GitHub repository offering an extensive collection of IT learning video tutorials. The repository is continuously updated and includes courses on popular programming languages like Java, Python, Go, C, C++, and C#. Additionally, it features video tutorials on front-end development, databases, big data, artificial intelligence, AIGC, ChatGPT, software testing, network security, reverse engineering, HarmonyOS application development, and Android. It serves as a valuable resource for developers, students, and IT professionals seeking to enhance their skills and stay current with the latest technologies.

ILearnDeepLearning.py is an open-source repository offering a collection of small projects focused on neural networks and deep learning. It serves as a practical companion to articles published on Medium, allowing users to explore the underlying mathematics and implementations of deep learning concepts. The repository includes projects on topics such as visualizing gradient descent, implementing neural networks with NumPy, preventing overfitting, and optimizing training processes. It also features examples of creating animated graphs and understanding convolutional neural networks. This resource is designed to help both beginners and those with some experience to deepen their understanding of deep learning through hands-on coding and visual explanations.

Baasin provides comprehensive AI education through its 'Baas in AI Academy,' offering online video courses like the ChatGPT Video Cursus. The platform also features an AI Shop for various AI-related products and a community, 'De AI Vereniging,' for networking and shared learning. Baasin caters to both individuals and businesses, with specific enterprise contracts available for teams, including options for email domain-based access and SSO. Beyond digital products, Baasin offers onsite AI workshops and a whitelabel workshop option, allowing organizations to deliver their own AI training using Baasin's materials. The platform emphasizes practical, immediately applicable knowledge to help users leverage AI for a competitive edge.

machineLearningDeepLearning is a GitHub repository dedicated to Li Hongyi's 2021 machine learning and deep learning course. It serves as a comprehensive resource for students and enthusiasts, offering lecture notes, presentation slides (PPTs), and homework assignments. The repository is actively maintained and updated, ensuring access to the latest course materials, including code examples in TensorFlow and PyTorch. Users can download the content via Git or directly from the website. It also provides links to course videos on Bilibili and data sets via Baidu Netdisk, making it a valuable self-study resource for those looking to deepen their understanding of machine learning and deep learning concepts.

Machine-Learning-Zoomcamp offers a comprehensive, free 4-month course designed to teach machine learning engineering from end-to-end. Participants learn to build regression and classification models in Python, work with key algorithms like linear/logistic regression, decision trees, and deep learning, and then deploy them using Docker, FastAPI, Kubernetes, and AWS Lambda. The course provides flexible learning paths, including a live cohort with deadlines, automatic homework scoring, and project peer reviews, or a self-paced option. All materials are freely available, including pre-recorded lectures and homework assignments. It's an ideal resource for individuals looking to gain practical ML engineering skills and build a portfolio.

BookAI is a mobile application developed by Big Mobile Games that leverages artificial intelligence to transform user ideas into professional, personalized e-books. With its intuitive interface, users can generate complete stories and books with just a few clicks. The application is designed to be user-centric, delivering simple, original, and useful experiences. Launched in September 2023, BookAI is available for free download on Google Play, making it accessible for anyone looking to quickly create unique content for personal use or sharing. It aims to simplify the book writing process, allowing users to take full author credit for their AI-generated creations.

Mathematics-for-ML is a comprehensive collection of resources designed to help individuals learn and review the mathematical foundations necessary for machine learning. This GitHub repository offers a curated list of books, academic papers, and video lectures covering key areas such as algebra, topology, differential calculus, optimization theory, linear algebra, probability, and statistics. It serves as an excellent starting point for anyone looking to build a strong mathematical understanding for deep learning and machine learning concepts. The resources range from introductory texts to more advanced topics, making it suitable for various learning levels.

The MATLAB-Simulink-Challenge-Project-Hub offers a curated collection of research and design project ideas, enabling users to gain practical experience and insight into current technology trends and industry directions. These projects are designed to contribute to engineering and science by addressing key industry challenges. Participants can explore various topics like Artificial Intelligence, Autonomous Vehicles, Robotics, and Sustainability. Upon project completion, individuals can receive official recognition from MathWorks research leads and rewards. The hub also provides an interactive filtering tool to help users find projects matching their interests by technology, application area, and MATLAB/Simulink tools. It emphasizes open and accessible work, with guidelines for generative AI use.

ML-foundations is a comprehensive educational resource, developed by Jon Krohn, that provides foundational knowledge essential for understanding contemporary machine learning approaches, including deep learning and other AI techniques. The curriculum is structured into eight subjects across four core areas: Linear Algebra, Calculus, Probability and Statistics, and Computer Science (Algorithms & Data Structures, Optimization). It offers practical, functional understanding through vivid illustrations, paper-and-pencil exercises with solutions, and hundreds of Python code examples in hands-on Jupyter notebooks, primarily focusing on PyTorch and TensorFlow libraries. The content is accessible through various channels including YouTube, O'Reilly, Udemy, and ODSC, catering to different learning preferences and offering additional features like interactive testing and certificates through paid options. It is ideal for data scientists, software developers, and AI enthusiasts looking to deepen their understanding of ML fundamentals.

ml-powered-applications serves as the official companion code repository for the O'Reilly book 'Building Machine Learning Powered Applications'. This open-source project offers a practical resource for software developers and machine learning engineers looking to understand and implement ML-powered applications. The repository includes a comprehensive set of Jupyter notebooks that illustrate key concepts covered in the book, a Python library (`ml_editor`) containing core functions for a Machine Learning-driven writing assistant case study, and a Flask application demonstrating how to serve ML results to users. It supports Python 3.6 and 3.7, with setup instructions for virtual environments and necessary package downloads (spaCy, NLTK).

Narrative Nooks leverages AI to deliver engaging and personalized learning experiences through interactive stories. Designed for young learners, the platform offers a wide variety of subjects and features to help children master new skills. Key offerings include dynamically generated lesson badges, custom story creation, and on-demand audio and image generation. Users also benefit from 24/7 on-call tutoring support, ensuring expert help is always available. The platform aims to captivate and educate through narrative-driven lessons, making learning both effective and enjoyable for students globally.

nndl offers a Chinese translation of Michael Nielsen's renowned online book, "Neural Networks and Deep Learning." This project serves as a valuable educational resource for Chinese-speaking individuals interested in the fundamentals of neural networks and deep learning. The translation is meticulously crafted in LaTeX, ensuring a high-quality visual presentation of mathematical equations and plots, which is crucial for understanding complex technical concepts. It also incorporates finished work from another translation project, demonstrating a collaborative approach to providing comprehensive learning materials. Users can compile the LaTeX source code to generate a PDF document, requiring a TeX system and specific fonts for optimal display.

paip-lisp is an open-source repository containing the complete Lisp code from Peter Norvig's influential 1992 textbook, "Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp." Released under an MIT license, this resource allows users to explore and run the original code examples covering a wide range of AI topics, from early AI programs like GPS and Eliza to advanced concepts such as logic programming, expert systems, and natural language processing. It's designed for interactive learning, requiring a Common Lisp interpreter/compiler environment to engage with the code directly. The repository also provides various formats of the book itself, including ebooks and scanned PDFs, making it a comprehensive educational package for students and researchers.

DocToQuiz is an AI-powered platform designed to streamline the creation and management of interactive quizzes. It can instantly convert a wide range of content formats, such as PDFs, Word documents, PowerPoint presentations, YouTube videos, audio files, images, and even web pages, into comprehensive quizzes. The tool is particularly beneficial for teachers and students, offering features like automatic grading and analytics to assess performance. It aims to simplify the assessment process, providing a quick and efficient way to generate educational content and evaluate understanding without manual effort.

Chapter 1 Quiz - Transformers Fundementals is an interactive quiz application designed to test users' understanding of the core concepts related to Transformers. Developed by huggingface-course, this tool allows individuals to assess their knowledge on a specific topic within the field of AI and machine learning. Users must log in to take the quiz, answer each question, and submit their responses. Upon successfully passing the quiz, participants receive a personalized certificate, making it an excellent resource for self-assessment and reinforcing learning. The platform is hosted on Hugging Face Spaces, ensuring accessibility and ease of use for anyone looking to validate their understanding of Transformers Fundementals.

Awesome-LLM-Inference is a comprehensive, curated list of research papers and associated code focused on Large Language Model (LLM) and Vision-Language Model (VLM) inference. This resource is designed for researchers and engineers looking to explore advanced techniques for optimizing LLM performance, including Flash-Attention, Paged-Attention, WINT8/4 quantization, and various parallelism strategies. The repository provides an organized collection of topics such as multi-GPU/multi-node parallelism, disaggregating prefill and decoding, KV cache scheduling, and long-context attention optimization. It also features sections on LLM algorithmic/evaluation surveys, inference frameworks, and specific topics like Mixture-of-Experts (MoE) LLM inference. Users can download all listed PDFs via a Python script, making it a valuable hub for staying updated on the latest advancements in efficient LLM inference.

Bloom Book is an AI tool available on Hugging Face Spaces, designed for text generation and related tasks. It leverages the Streamlit framework to create interactive data applications, providing a platform for users to explore and utilize AI models. While the live website currently shows a runtime error, indicating it may not be fully operational at this moment, its intended purpose is to facilitate engagement with AI-powered text generation. The tool is part of the bigscience initiative, aiming to make advanced machine learning applications accessible to the community.

Awesome-LLM-3D is a comprehensive, curated list of academic papers focusing on the intersection of Large Language Models (LLMs) and 3D-related tasks. This repository serves as a valuable resource for researchers and academics interested in 3D understanding, reasoning, generation, and embodied agents, as well as other foundational models like CLIP and SAM. It is actively maintained, with regular updates to include the latest advancements in the field. The list is organized by various sub-topics, including 3D Unified Understanding and Generation, 3D Understanding (LLM and other Foundation Models), 3D Reasoning, 3D Generation, 3D Embodied Agents, and 3D Benchmarks, making it easy to navigate and find relevant literature. The project also highlights recent news and survey papers in the domain.