Research & Education

Self-Driving-Cars is an open-source repository hosted on GitHub, offering a comprehensive collection of Coursera open courses from the University of Toronto. This resource is specifically designed for individuals interested in the field of self-driving car technology, providing access to videos, subtitles, and PDF materials. It's particularly beneficial for postgraduate students and researchers aiming to work on automotive motion planning, offering a structured and in-depth learning experience. The repository includes courses covering topics from an introduction to self-driving cars to state estimation, visual perception, and motion planning. Users can download and watch the content, and a rough notebook based on subtitles is provided for better review.

stat479-machine-learning-fs19 offers comprehensive course material for the STAT 479: Machine Learning class taught by Sebastian Raschka at the University of Wisconsin-Madison. This GitHub repository serves as a central resource for students, covering a wide array of machine learning concepts from introductory topics like K-Nearest Neighbors to advanced subjects such as ensemble methods, model evaluation, and dimensionality reduction techniques. The material is organized into lectures, including practical computational foundations using Python, Anaconda, Jupyter Notebooks, NumPy, SciPy, and Scikit-Learn. It's an invaluable resource for students and educators looking for structured machine learning curriculum.

theMLbook is an open-source GitHub repository offering Python code designed to replicate the illustrations found in 'The Hundred-Page Machine Learning Book'. This resource is invaluable for students and professionals seeking to deepen their understanding of machine learning concepts through practical, visual examples. By providing the exact code used for the book's figures, theMLbook allows users to interact directly with the algorithms and models discussed, facilitating a hands-on learning experience. It covers a range of machine learning topics, from fundamental algorithms like linear regression and K-means to more advanced concepts such as autoencoders and UMAP, making it a comprehensive companion for the book's readers.

awesome-NeRF-papers is an Open Source repository that serves as a comprehensive collection of research papers related to Neural Radiance Fields (NeRF). It meticulously gathers publications from top-tier computer vision and machine learning conferences, including CVPR, ICCV, ECCV, NIPS, ICML, and ICLR. This resource is invaluable for researchers, academics, and students who need to track the rapid developments in NeRF technology. The repository is organized by conference and year, making it easy to navigate and find specific papers. It also includes summaries and counts of papers from various conferences, offering a quick overview of research trends and the volume of work being published in this field.

awesome-programming-books is a meticulously curated list of programming books, offering a wide array of topics essential for both aspiring and experienced developers. This resource encompasses fundamental areas such as Algorithms and Data Structures, Artificial Intelligence, Software Architecture, and Human–Computer Interaction. It also delves into specialized fields like Operating Systems, Database Systems, IT Security, Concurrency, Interpreters and Compilers, High-Performance Computing, Distributed Systems, Game Development, and Mathematical Optimization. Each category provides a selection of highly-regarded books, complete with ISBNs, making it an invaluable guide for students, educators, and professionals looking to deepen their knowledge or explore new domains within computer science and software engineering.

Bambot is an open-source project designed to make AI robotics accessible and easy to use. It provides a platform for individuals to experiment with and develop AI-powered robotic systems using low-cost components. The project aims to lower the barrier to entry for AI robotics, allowing users to build and interact with their own AI robots. It includes resources and code to facilitate the creation and control of these robots, making it an ideal tool for learning and prototyping in the field of AI and robotics.

The 'deploying-machine-learning-models' repository offers comprehensive code and materials for an online course focused on the deployment of machine learning models. This open-source resource is designed to accompany the Udemy course "Deployment of Machine Learning Models," providing practical examples and guidance for students. It includes various sections covering research and development, production model packaging, model serving APIs, continuous integration, and deployment with containers. The repository is primarily written in Jupyter Notebook and Python, making it an invaluable tool for those looking to understand and implement machine learning model deployment strategies.

Debaters.ai is a domain registered at Dynadot.com, with a website currently under construction. The homepage, pricing, plans, features, FAQ, and documentation pages all display a 'Website coming soon' message, indicating that the platform is not yet live. Users visiting the site are met with a loading screen and a message stating, 'We’re getting things ready. Loading your experience… This won’t take long.' As such, no specific features, pricing models, or use cases can be determined from the current live content. The tool's intended purpose, as an AI-powered platform to enhance critical thinking and advocacy skills, is derived from its stored description, but this is not reflected on the live site.

Introduction to Machine Learning with Python is a comprehensive open-source repository designed to accompany the book of the same name by Andreas Mueller and Sarah Guido. It provides all the notebooks and code examples used in the book, making it an invaluable resource for students and practitioners looking to learn machine learning with Python. The repository includes helper functions from the `mglearn` library for creating figures and datasets, and all necessary datasets are included, with the exception of `aclImdb`. Users can set up their environment using `conda` or `pip` to install required packages like `numpy`, `scipy`, `scikit-learn`, `matplotlib`, `pandas`, `pillow`, and `graphviz`. It also supports `nltk` and `spacy` for text processing chapters.

Chatbots Magazine, founded in 2016, serves as a comprehensive resource for individuals interested in the rapidly evolving fields of artificial intelligence. It offers information and insights specifically focused on bots, chatbots, Natural Language Processing (NLP), and machine learning. The platform aims to educate its audience on various topics within the AI space, providing a deeper understanding of these technologies and their applications.

GVHMR is an AI tool hosted on Hugging Face Spaces that specializes in 3D human pose estimation and visualization. Users provide input images, and the application processes them to output detailed 3D pose information. The tool sets up its necessary environment by downloading models and dependencies to perform its core function. While the live website indicates a runtime error, the intended functionality is to provide advanced human pose analysis, making it valuable for researchers, developers, and anyone interested in computer vision applications related to human movement and form.

The MIT Deep Learning Book in PDF format is a valuable resource for anyone interested in the field of deep learning. Compiled by Janishar Ali, this repository offers the complete text by Ian Goodfellow, Yoshua Bengio, and Aaron Courville in a convenient PDF format. While the original book is available as a free HTML version, this project addresses the lack of an official PDF download by providing a 'flawless PDF version' suitable for printing. Users can access the entire book as a single PDF or download individual chapters. This resource is ideal for students, researchers, and practitioners seeking a comprehensive and portable reference for deep learning concepts.

Machine-Learning-in-Action is an open-source GitHub repository offering practical code implementations for various machine learning algorithms, all based on the popular book "Machine Learning in Action." Developed in Python 3, this resource is designed to help users understand and apply machine learning concepts through hands-on examples. The repository includes code for algorithms such as K-Nearest Neighbors, Decision Trees, Naive Bayes, Logistic Regression, Support Vector Machines, AdaBoost, and different regression techniques. It also provides datasets to accompany the code, making it a comprehensive learning resource for students and developers looking to deepen their understanding of machine learning.

makeyourownneuralnetwork is an open-source code repository hosted on GitHub, designed to accompany the 'Make Your Own Neural Network' book. It offers practical examples and implementations of neural network concepts, making it an invaluable resource for individuals looking to learn and understand the fundamentals of neural networks through hands-on coding. The repository includes various Jupyter Notebooks covering topics such as MNIST dataset handling, neural network implementation, loading custom images, and backquerying. This resource is ideal for students and self-learners who want to dive deep into the mechanics of neural networks and build their own models from scratch.

nn-from-scratch is an open-source project available on GitHub that provides a practical implementation of a neural network from scratch. This resource is designed for individuals looking to deepen their understanding of how neural networks function at a foundational level. The project includes Python code, an iPython notebook for interactive learning, and a related blog post that explains the concepts in detail. It covers the setup of a virtual environment and installation of necessary requirements, making it accessible for hands-on learning and experimentation with neural network architectures.

Polyglot Media offers a suite of experimental AI language learning tools designed to assist both students and teachers. The platform features a Vocabulary Lesson Generator that creates comprehensive lessons with warm-up questions, definitions, example sentences, exercises, follow-up questions, writing prompts, and answer keys. Additionally, it provides a Vocabulary List Generator for quick vocabulary compilation and a Resource Finder to locate language learning materials. For English learners, there's a Grammar Lesson Generator complete with explanations, grammar tables, examples, exercises, and answer keys. The Reading Exercise Generator creates passages with definitions and comprehension questions, making it a versatile tool for language educators and learners alike. Users are advised to use these generators with a qualified teacher to verify the output.

The University of Science and Technology of Hanoi (USTH) is an international public university in Vietnam, founded under an intergovernmental agreement between Vietnam and France. USTH provides a global educational environment with programs taught in English, focusing on leading science and technology fields relevant to the 4.0 era. The university offers 20 undergraduate programs, 7 master's programs (including dual-degree options with French partners), and 7 doctoral programs, all with internationally recognized academic value. USTH emphasizes research and development, with a strong connection to industry and international collaborations, particularly with French institutions. Key highlights include a high employment or further study rate for graduates (97%) and a significant percentage of faculty holding doctoral degrees (84%).

10,000 Original AI Godfathers is presented as a digital time capsule, highlighting individuals who played significant roles in the foundational stages of artificial intelligence. This initiative aims to document and recognize researchers, scientists, engineers, investors, and dedicated AI users, collectively referred to as 'AI Godfathers.' The project seeks to preserve the legacy of these early contributors, offering insights into the diverse talents and efforts that shaped the nascent AI landscape. While the website content currently displays a parked domain message, the original concept suggests a focus on historical documentation and recognition within the AI community.

Autopilot-Notes is a comprehensive open-source knowledge base designed for systematic learning and mastery of autonomous driving technology. It covers a wide array of topics including foundational theories, hardware components, perception algorithms, localization techniques, planning strategies, and control systems. The repository also features in-depth analyses of solutions from leading manufacturers like Tesla, Baidu Apollo, and Huawei ADS. With daily updates on industry news and technical advancements, Autopilot-Notes serves as an invaluable resource for students and developers looking to stay current with the rapidly evolving field of autonomous vehicles. It emphasizes practical application with content on simulation, deployment, and optimization.

DeepLearningTutorials is a valuable resource for anyone looking to delve into the field of deep learning. It provides detailed tutorial notes and corresponding Python code, specifically designed to introduce users to some of the most important deep learning algorithms. The tutorials emphasize learning multiple levels of representation and abstraction, crucial for processing data like images, sound, and text. A key feature is its integration with Theano, a Python library that simplifies deep learning model development and offers the capability to train models efficiently on a GPU. The project is hosted on GitHub, ensuring accessibility to its code and documentation, and encourages users to browse the tutorials online for an optimal learning experience.

deep-learning-from-scratch-4 is an open-source GitHub repository that serves as the support site for the book "Deep Learning from Scratch 4: Reinforcement Learning Edition" (O'Reilly Japan, 2022). It provides all the source code used in the book, organized by chapter, along with common utility code. The repository also offers Jupyter Notebook versions of the code, which can be run directly on cloud services like Google Colab, Kaggle Notebook, and Studio Lab for interactive learning. It supports Python 3.x and requires libraries such as NumPy, Matplotlib, OpenAI Gym, and DeZero (or PyTorch). The project is licensed under the MIT License, allowing for free commercial and non-commercial use, making it an excellent resource for students and developers exploring reinforcement learning.

InterpretableMLBook is the Chinese translation of "Interpretable Machine Learning" by Christoph Molnar, a highly regarded work in the field of interpretable machine learning. This book serves as a comprehensive guide to understanding the interpretability of black-box models, making complex concepts accessible to a Chinese-speaking audience. It systematically organizes interpretability methods, describing each through intuitive language and detailed mathematical formulas. A key feature is the practical application of each method to real-world data, allowing readers to truly grasp their utility. The book also includes critical discussions on the advantages and disadvantages of various methods, making it a valuable resource for both technical practitioners and researchers.

Machine-learning-engineering-for-production-public serves as the official public repository for DeepLearning.AI's Machine Learning Engineering for Production Specialization. This resource is designed to support students and professionals in understanding the intricacies of deploying machine learning models into real-world production environments. The repository contains various materials, including course content, labs, and other public resources relevant to the specialization's curriculum. While it provides valuable learning assets, the repository is currently not accepting pull requests for contributions. It is an essential companion for anyone undertaking the DeepLearning.AI MLEP Specialization, offering practical insights and foundational knowledge for machine learning engineering.

Machine-Learning-Interviews is an open-source GitHub repository designed to guide individuals through the preparation process for Machine Learning and AI technical interviews. This resource is particularly valuable for those targeting roles like Machine Learning Engineer and Applied Scientist at prominent tech companies. It covers essential interview modules such as general coding (algorithms and data structures), ML-specific coding, ML fundamentals, ML system design, and behavioral questions. The repository also includes a new section on Agentic AI Systems, reflecting the latest trends in AI engineering. Compiled from the author's personal experience and successful interview preparations, it offers a structured approach to mastering the diverse components of technical ML interviews.