Research & Education

Towards AI, Inc. offers comprehensive AI education, training, and custom AI system development for both individuals and organizations. Their platform is designed to guide users from initial AI curiosity to achieving tangible AI capabilities. They provide a range of resources including courses, community engagement, enterprise training programs, and bespoke AI development services. Since 2019, Towards AI has educated over 400,000 individuals, focusing on practical AI engineering courses and corporate AI bootcamps. The company aims to empower users to effectively utilize and build with AI tools and models, bridging the gap between theoretical knowledge and real-world application.

PyTorchStepByStep is the official GitHub repository for the book "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide." This open-source resource offers a comprehensive collection of Jupyter notebooks, with one notebook corresponding to each chapter of the book. Users can run these notebooks to reproduce the code examples and outputs presented in the book, fostering a hands-on learning experience. The repository has been revised for PyTorch 2.x, addressing updates in PyTorch, Torchvision, HuggingFace, and other libraries. It provides flexible options for execution, including Google Colab, Binder for cloud-based access, and detailed instructions for local installation using Anaconda, Conda environments, PyTorch, TensorBoard, and optional GraphViz/TorchViz setup.

pyprobml is an open-source Python code repository designed to accompany Kevin Murphy's "Probabilistic Machine Learning: An Introduction" and "Probabilistic Machine Learning: Advanced Topics" books. It provides Python 3 code to reproduce all the figures presented in these textbooks, utilizing widely-used libraries such as NumPy, SciPy, Matplotlib, and scikit-learn. Additionally, the codebase incorporates modern machine learning frameworks like JAX, TensorFlow 2, and PyTorch for more advanced topics. The project is currently in maintenance mode and offers various ways to run the notebooks, including Google Colab for cloud-based execution with free GPU/TPU access, or locally after installing dependencies. It also provides utility code via probml-utils and guidance for contributions.

OWL Learning, powered by MPS, provides expert online learning solutions for all learners. With over 50 years of experience, OWL Learning partners with academic and industry partners to develop content and learning experiences for learners at any level and delivery in any modality on any platform. They offer a full suite of services including content development, learning design, creative media services, extended reality and gamification, translation, AI/ML services, and accessibility. OWL Learning delivers scaled solutions to meet development needs, whether for consultation to develop a vision or expertise to scale development.

Own Tutor is an AI-powered educational platform designed to offer personalized learning experiences to students. The tool enables schools to develop custom AI tutors tailored to the specific needs of individual students, fostering a learning environment where students can progress at their own pace. By providing personalized guidance and support, Own Tutor aims to enhance understanding and improve academic success. The platform also supports the creation of virtual schools and offers features for managing lessons, making it a comprehensive solution for educational institutions looking to integrate AI into their teaching methodologies.

NASSCOM serves as the apex body for India's $315 billion technology industry, encompassing over 3,000 member companies across services, products, and startups. The organization plays a crucial role in policy advocacy, shaping regulations that foster innovation and technological advancement. It provides valuable industry knowledge through flagship publications and insights, empowering members with a deeper understanding of both the Indian tech landscape and the global economy. NASSCOM also emphasizes skilling and training, co-creating programs to develop industry-ready talent and establish India as a digital hub. Furthermore, it facilitates powerful connections among global innovators and visionaries through various events, promoting collaboration and growth opportunities within the tech sector.

Speech-Separation-Paper-Tutorial is an invaluable resource for anyone interested in speech separation based on neural networks. This GitHub repository compiles a comprehensive collection of papers, models, and related resources spanning from 2016 to 2025. It offers detailed overviews, model timelines, and performance comparisons across various datasets like WSJ0-2Mix, WHAM!, and LibriMix. Users can explore different model categories, including deterministic vs. generative approaches, network architectures like dual-path and Conv-TasNet, and learning methods such as predictive and unsupervised techniques. The tutorial also delves into multi-modal speech separation, evaluation metrics like SI-SNRi and SDRi, and provides information on key datasets, making it a central hub for academic research and development in the field.

Slice Knowledge AI offers a secure and private AI environment specifically designed for organizations to manage and leverage their internal knowledge. This platform enables businesses to organize vast amounts of information, fostering improved performance and efficiency across teams. A key differentiator is its commitment to data privacy, ensuring that all organizational data remains strictly within the client's domain. This makes Slice an ideal solution for companies with stringent security and compliance requirements, allowing them to harness the power of AI without compromising sensitive information. It's built to help organizations enhance their operational intelligence and decision-making capabilities through a controlled AI ecosystem.

Teachable Machine Community is an open-source repository offering example code snippets and machine learning code for Teachable Machine. Teachable Machine is a web-based tool designed to make machine learning model creation fast, easy, and accessible for everyone, including educators, artists, students, and innovators. Users can train a computer to recognize images, sounds, and poses without needing prior machine learning knowledge or coding. The repository includes a libraries section with machine learning code utilizing Tensorflow.js for in-browser model training and execution, along with API helper libraries for integrating exported models into projects. It also features a snippets section with code and instructions for using Teachable Machine models in languages like Javascript, Java, and Python.

Time-Series-Forecasting-and-Deep-Learning is a comprehensive, open-source GitHub repository dedicated to curating resources for time series forecasting and deep learning. It serves as a valuable hub for researchers, data scientists, and students seeking to explore the latest advancements in the field. The repository meticulously organizes research papers, including those from 2017 up to 2026, alongside benchmarks, applications like TimeGPT, and various datasets. Additionally, it provides links to relevant courses, blogs, and code libraries, making it an all-in-one reference for anyone involved in time series analysis and model development. The structured content, including a table of contents, allows for easy navigation through a vast collection of academic and practical materials.

tf2_course is a comprehensive collection of Jupyter notebooks designed to accompany the "Deep Learning with TensorFlow 2 and Keras" training. This open-source project, available on GitHub, provides practical exercises and their corresponding solutions, making it an invaluable resource for individuals looking to deepen their understanding and skills in deep learning using TensorFlow 2 and Keras. Users can access these notebooks online via services like Colaboratory, Binder, or Deepnote for temporary environments, or install them locally for a persistent setup. The project also includes detailed installation instructions and addresses common issues like Python version compatibility and SSL errors, ensuring a smooth learning experience for students and professionals alike.

NXTLVL provides an AI-driven learning experience designed for children aged 7-11, focusing on developing essential problem-solving skills for the future. The platform offers online mini-missions led by a 'superhuman AI coach' that act as intellectual sparring partners, providing advice and helping children reflect on their actions. These 20-minute daily learning experiences are designed to be fun and effective, allowing kids to learn independently and level up their skills. Additionally, NXTLVL offers live team sessions facilitated by human instructors, where children collaborate to solve complex problems, enhancing their communication and collaboration skills. Parents can monitor progress through weekly AI Highlights and have the option to add 1:1 sessions with a human coach for deeper engagement. The program is developed by experts in education technology and includes a Problem Solving Olympiad for schools.

aima-exercises is an interactive and collaborative open-source platform designed to digitalize the exercises from the renowned textbook "Artificial Intelligence: A Modern Approach" by Stuart J. Russell and Peter Norvig. This platform serves as the exclusive online repository for exercises for the book's fourth edition, allowing students to solve them and teachers to contribute new ones. It leverages Jekyll 3 and Ruby 2.5, providing a structured environment with dedicated folders for figures, JavaScript codes, LaTeX files, and Markdown exercises. The project aims to foster a collaborative learning environment for AI education, making complex AI concepts accessible through practical exercises.

This GitHub repository, machine-learning-for-software-engineers, offers a detailed, multi-month study plan designed for software engineers looking to transition into machine learning engineering. The plan emphasizes a top-down, results-first approach, focusing on practical application and abstracting much of the underlying mathematics for beginners. It includes a curated list of video resources, books (beginner and practical), MOOCs, Kaggle competitions, and communities. The resource also provides insights into prerequisite knowledge, daily study routines, and motivation, making it a comprehensive guide for self-taught learners aiming for a career in ML.

Fundamentals-of-Deep-Learning-Book serves as the official code companion for the O'Reilly "Fundamentals of Deep Learning, Second Edition" book. This GitHub repository offers practical, PyTorch-based implementations of all algorithms presented in the book, making it an invaluable resource for those looking to apply deep learning concepts. The code is primarily provided as Google Colab notebooks, allowing users to run examples directly from the repository without extensive setup. Additionally, some examples include .py files for more convenient execution. It also archives code from the first edition, ensuring comprehensive coverage for different versions of the book. This resource is ideal for students and practitioners aiming to deepen their understanding of deep learning through hands-on coding.

GNNs-Recipe is a comprehensive study guide designed to help students and practitioners learn about Graph Neural Networks (GNNs). Hosted on GitHub, this resource offers a concise yet thorough overview of GNNs, covering foundational concepts, advanced topics, and practical applications. It includes a gentle introduction to GNNs, links to essential survey papers for a broader understanding, and recommendations for diving deeper into the subject with books and courses. The guide also points to valuable resources for staying updated with recent methods, paper implementations, benchmarks, and datasets, making it an invaluable tool for anyone looking to master GNNs.

mlcourse is a comprehensive collection of machine learning course materials hosted on GitHub, offering a wide range of resources for learning fundamental machine learning concepts and techniques. The repository includes detailed lectures, homework assignments with programming problems, and conceptual checks. Topics covered span supervised and unsupervised learning, model evaluation, regularization techniques like Lasso and Elastic Net, kernel methods, and Bayesian statistics. It also features discussions on advanced topics such as gradient boosting, backpropagation, and various optimization methods. The materials are suitable for students and aspiring data scientists looking to deepen their understanding of machine learning principles through practical exercises and theoretical explanations.

Learn_Computer_Vision offers a comprehensive, open-source curriculum designed to teach computer vision, based on a YouTube series by Siraj Raval. The program is structured over eight weeks, with daily study recommendations, and covers topics from low-level image processing to modern deep learning techniques like GANs. Each week includes video lectures, reading assignments, and practical projects using tools like Python, OpenCV, and TensorFlow. The curriculum aims to equip learners with the skills necessary to pursue careers in computer vision, whether through startups, consulting, or full-time employment. Prerequisites include basic Python, Calculus, and Linear Algebra.

Packback is an Instructional AI platform designed to enhance student engagement, strengthen writing skills, and improve retention in both higher education and K-12 settings. It offers three core products: Packback Discussions for inquiry-based student discussions with AI coaching, Packback Writing for real-time, formative writing feedback and AI-assisted evaluation, and Originality for proactive plagiarism prevention that empowers students to revise before submission. The platform aims to reduce faculty workload by handling routine feedback, allowing instructors to focus on mentorship. Packback's proprietary Instructional AI engine, built on pedagogical principles, provides actionable feedback without generating content for students, ensuring academic integrity and responsible AI use.

MODULABS is a comprehensive platform dedicated to AI and SW education, offering a variety of programs designed for practical, real-world application. The platform provides bootcamps like AI Engineer and AI Researcher courses, national support education, and tailored corporate training solutions. MODULABS emphasizes community-driven learning, fostering research communities and offering resources like 'LAB' for open research and 'MODUMOIM' for collaborative growth. It also features online courses, free seminars, and a blog with the latest AI content, making it a versatile hub for individuals and organizations looking to advance their AI and software skills.

ml-glossary is an open-source, community-maintained machine learning glossary designed to provide clear and accessible explanations of ML terms and concepts. It aims to present content in the most accessible way possible, with a heavy emphasis on visuals, interactive diagrams, code snippets (Python/Numpy), and equations formatted with Latex. The project encourages contributions from the community, allowing users to submit pull requests or raise issues to correct errors or add new content. It also provides a style guide for contributions, ensuring consistency and quality across entries. The glossary is a valuable resource for anyone looking to understand or contribute to machine learning knowledge.

ML_for_Hackers is a GitHub repository that hosts all the code examples accompanying the book "Machine Learning for Hackers" (2012). This resource is designed for individuals looking to gain practical experience with machine learning algorithms. The repository includes code for various topics such as Introduction, Exploration, Classification, Ranking, Regression, Regularization, Optimization, PCA, MDS, Recommendations, SNA, and Model Comparison. Users can get started by installing necessary R libraries, including RCurl and XML, using the provided `package_installer.R` script. While the code may have minor modifications since publication, it remains a valuable tool for learning and applying machine learning techniques.

mlbook is a free online book titled "Machine Learning from Scratch" available as a GitHub repository. This resource offers a comprehensive guide to understanding machine learning concepts and algorithms, making it accessible for self-study. The repository includes the full book content, a PDF version, and encourages community contributions through pull requests to the gh-pages branch. It's an excellent resource for individuals looking to delve into machine learning fundamentals, providing both theoretical knowledge and practical insights through its open-source nature and Jupyter Notebook content.

Machine-learning-specialization-andrew-ng is a comprehensive repository offering notes and practical implementations of machine learning algorithms, directly aligned with Andrew Ng's renowned machine learning specialization. This resource is structured around three core courses: Supervised Machine Learning (Regression and Classification), Advanced Learning Algorithms, and Unsupervised Learning, Recommenders, and Reinforcement Learning. It includes programming assignments completed using Jupyter Notebooks and Python, with clearly marked code sections for easy modification. The repository also provides detailed notes, high-level overviews, practical tips, and mathematical concept walkthroughs, making it an invaluable study aid for anyone delving into machine learning.