Research & Education

The LangChain-Chinese-Getting-Started-Guide is an open-source tutorial designed to help Chinese speakers learn and utilize the powerful LangChain framework. It covers essential concepts such as LLM invocation, prompt management, document loaders, text splitters, vector stores, chains, and agents. The guide provides practical examples, including performing Q&A with OpenAI models, integrating with Serpapi for internet searches, and summarizing long texts. It also addresses common challenges like API token limits and offers solutions using LangChain's features. The tutorial is actively maintained on GitHub, with updates and code examples available for hands-on learning.

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.

pml2-book, or "Probabilistic Machine Learning: Advanced Topics," is a comprehensive book authored by Kevin Murphy, focusing on advanced concepts within probabilistic machine learning. This resource is openly available as a PDF, primarily distributed through its GitHub repository, allowing for easy access and tracking of downloads and issues. It is designed for individuals who already possess a foundational understanding of machine learning and are looking to delve into more complex and specialized areas. The repository also includes various supplementary materials such as prefaces and tables of contents, offering a detailed overview of the book's structure and content.

DictionaryByGPT4 is an AI-generated English vocabulary resource created using GPT-4, offering comprehensive analysis for over 8000 words. Each entry includes detailed definitions, multiple example sentences, in-depth etymology (root and affix analysis), cultural background, word transformations (nouns, verbs, adjectives, tenses), memory techniques, and short illustrative stories. This tool aims to enhance language learning by providing a rich, contextual understanding of words, moving beyond rote memorization. It is available in various formats including EPUB, PDF, online web pages, JSON data, and an MDX dictionary, making it accessible for diverse learning preferences.

CourseraML is an open-source project that re-implements Andrew Ng's Machine Learning course assignments from Coursera using Python and Jupyter notebooks. This tool serves as a valuable resource for individuals looking to deepen their understanding of machine learning through hands-on practice. By providing the assignments in a Python-native, interactive notebook format, CourseraML caters to those who prefer working within the Jupyter ecosystem. It offers a practical, code-centric approach to learning core machine learning concepts, making it an excellent supplementary resource for students, researchers, and self-learners in the field.

Illustrated Machine Learning is a website dedicated to simplifying complex Machine Learning concepts through clear and concise illustrations. It aims to provide a visual learning resource for a diverse audience, including students, professionals, and individuals preparing for technical interviews. The platform focuses on making the underlying theories of Machine Learning more accessible and easier to understand, serving as a valuable aid for both beginners and seasoned professionals looking to refresh their knowledge. The website encourages community involvement, inviting users to report any mistakes and contribute to its development, fostering a collaborative learning environment.

Machine-Learning-Algorithms-Materials is an open-source GitHub repository offering a comprehensive collection of educational materials focused on machine learning algorithms. The repository contains various resources, including PDF documents explaining concepts like Simple Linear Regression, Ridge and Lasso Regression, Logistic Regression, Naive Bayes, and Decision Trees. Additionally, it provides practical implementation examples in Jupyter Notebooks for algorithms such as Linear Regression, Logistic Regression, and Ridge and Lasso. This resource is ideal for individuals looking to deepen their understanding and practical skills in machine learning, covering topics from fundamental regression techniques to advanced tree-based models and cross-validation methods.

Machine-Learning-Session is a GitHub repository offering a comprehensive series of introductory machine learning videos, primarily focused on foundational concepts and mathematical derivations. The repository includes PDF notes for each session, covering topics such as linear regression, linear classification, dimensionality reduction, support vector machines, kernel methods, and various probabilistic models like Gaussian Mixture Models and Hidden Markov Models. It also delves into advanced topics like variational inference, MCMC, Kalman filters, particle filters, and restricted Boltzmann machines. The content is designed to provide a solid stepping stone for individuals new to machine learning, aiming to clarify complex theories through detailed explanations and derivations.

dlaicourse is a comprehensive collection of open-source notebooks specifically designed for individuals looking to learn deep learning. Hosted on GitHub, this resource provides practical examples and exercises, making it an accessible platform for collaborative learning and modification. The notebooks cover various aspects of deep learning, including TensorFlow Deployment and TensorFlow In Practice, with specific examples like Cats v Dogs Augmentation and RockPaperScissors. It's an ideal resource for AI enthusiasts and students who want to enhance their understanding and practical skills in deep learning through hands-on coding examples.

machine-learning-coursera-1 is an open-source GitHub repository dedicated to housing all the coursework and assignments completed as part of Coursera's Machine Learning Course. This repository acts as a valuable resource for students and learners who wish to review, understand, or reference implementations related to the course material. It provides a structured collection of files, organized by week, covering various assignments and projects undertaken during the machine learning curriculum. The repository is publicly accessible, allowing anyone interested in machine learning to explore the practical applications and solutions developed within the course context.

Machine-Learning-in-90-days is an open-source GitHub repository designed to guide users through learning machine learning concepts over a 90-day period. It provides a structured curriculum, including a Python crash course, to help individuals build a strong foundation in machine learning. The repository is suitable for both students and professionals looking to acquire or enhance their skills in this field, offering practical resources and a clear learning path. As a GitHub repository, it leverages the open-source community for contributions and ongoing development.

dlbook_exercises is an open-source GitHub repository offering a collection of exercises designed to complement the Deep Learning textbook available at www.deeplearningbook.org. This resource is invaluable for students and researchers looking to deepen their understanding and practical application of deep learning principles. The exercises cover various topics within the textbook, allowing users to engage with the material through hands-on problem-solving. Being open-source, it provides a flexible and accessible platform for learning and collaboration, enabling users to contribute or adapt the exercises to their specific needs. It serves as a practical companion to the theoretical knowledge presented in the textbook, enhancing the overall learning experience.

Python-for-Probability-Statistics-and-Machine-Learning is an open-source collection of Jupyter Notebooks designed to accompany the Springer book "Python for Probability, Statistics, and Machine Learning." This resource provides practical, code-based examples for understanding and applying core concepts in probability, statistics, and machine learning using Python. The notebooks are updated for Python 3.6+ and cover a wide range of topics, making it an invaluable learning aid for students and professionals. Users can explore various statistical analyses, probability theories, and machine learning algorithms directly through interactive Jupyter environments, facilitating hands-on learning and experimentation.

The stat453-deep-learning-ss21 repository on GitHub serves as the official course material hub for STAT 453: Intro to Deep Learning at UW-Madison, specifically for the Spring 2021 semester. It offers a comprehensive collection of resources, including Jupyter Notebooks and Python code, covering various deep learning topics. The repository is structured with folders for each lecture (e.g., L01, L03, L05), making it easy for students to navigate and access relevant content. This open-source resource is ideal for students and educators looking for practical examples and foundational knowledge in deep learning.

visual-machine-learning-notes is an open-source repository offering a comprehensive collection of visual sketch notes from various Machine Learning conferences and summer schools. Curated by Robert T. Lange, these notes provide an accessible and engaging way to review and understand complex machine learning topics. The repository includes notes from events like NeurIPS, ICLR, MLSS, and more, dating back to 2017. Users can explore detailed visual summaries, often accompanied by links to original programs and slides. The project also provides insights into the note-taking process, including recommended tools and advice for effective visual learning. It serves as a valuable resource for students, researchers, and anyone looking to deepen their understanding of machine learning through a visual medium.

tiepvupsu.github.io hosts a comprehensive Machine Learning blog, primarily in Vietnamese, offering a wealth of educational content for AI learners. The platform is open-source and maintained on GitHub, allowing for community contributions and transparency. It features various resources including blog posts, code examples, and PDF documents covering topics like machine learning mathematics and matrix analysis. The blog aims to make complex machine learning concepts accessible, providing practical insights and theoretical foundations through its articles and shared materials. It's a valuable resource for individuals looking to deepen their understanding of machine learning.

DeepLearningFromScratch is a GitHub repository that serves as a companion to the book "Deep Learning from Scratch: Building with Python." It provides the electronic version of the book along with all the corresponding code examples, organized by chapter. This resource is ideal for individuals looking to understand and implement deep learning concepts using Python, NumPy, and Matplotlib. The repository includes source code for each chapter, common utilities, and necessary datasets, making it a practical guide for hands-on learning. It operates under an MIT license, allowing for free use in both commercial and non-commercial contexts, and includes an errata for corrections.

udlbook is an open-source GitHub repository dedicated to the book "Understanding Deep Learning" by Simon J.D. Prince. It serves as a comprehensive educational resource, providing a wealth of materials for students and educators alike. The repository includes Jupyter notebooks that allow for interactive learning and experimentation with deep learning models, as well as course slides for lectures and presentations. Additionally, it contains PDF figures, errata, and an answer booklet for students, making it a complete package for studying deep learning. The content is designed to offer a thorough understanding of deep learning principles, from foundational concepts to advanced topics, and is freely available under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License.

PythonNumericalDemos is an open-source repository designed to provide Python demonstrations for spatial data analytics. It encompasses a range of topics, including geostatistical and machine learning workflows, making it a valuable resource for both students and educators. The repository is specifically tailored to support courses in data analytics and geostatistics, helping users overcome intellectual hurdles in data science. By offering practical, code-based examples, PythonNumericalDemos facilitates a deeper understanding of complex numerical methods and their application to real-world spatial data problems. Its open-source nature encourages collaboration and continuous improvement within the data science community.

Deep-learning-for-image-processing is an open-source educational resource designed to help users understand and implement deep learning techniques for image processing. It offers comprehensive tutorials and practical implementations across various domains, including image classification, object detection, semantic segmentation, instance segmentation, and keypoint detection. The resource leverages popular frameworks like PyTorch and TensorFlow (specifically Keras module in TensorFlow2) to demonstrate network architectures and training processes. It includes detailed explanations of models such as LeNet, AlexNet, VggNet, ResNet, YOLO series, FCN, DeepLabV3, Mask R-CNN, and DeepPose. All course materials, including PPTs and code, are provided, making it a valuable asset for students and researchers in the field.

deeplearningbook-chinese is a collaborative, open-source initiative dedicated to translating the seminal 'Deep Learning' book into Chinese. This project aims to make complex deep learning concepts accessible to a broader Chinese-speaking audience. It emphasizes community involvement, encouraging readers to contribute suggestions and pull requests to continuously improve translation accuracy and readability. While a PDF version is available for direct download, the project also supports the official published paper version. The project highlights the importance of open access to knowledge and the collective effort in refining technical translations, making it a valuable resource for students and researchers alike.

100-Days-of-ML-Code-Chinese-Version is an open-source project offering a Chinese translation of machine learning infographics and code implementations, designed to help users learn and practice machine learning concepts. The resource provides a structured curriculum covering a wide range of topics, including data preprocessing, various linear regression models, logistic regression, K-nearest neighbors (k-NN), Support Vector Machines (SVM), decision trees, and random forests. It also delves into unsupervised learning with K-means and hierarchical clustering. Beyond theoretical explanations, the project includes practical code implementations, deep dives into essential libraries like NumPy, Pandas, and Matplotlib, and foundational mathematical concepts such as linear algebra and calculus, making it a comprehensive learning companion for aspiring machine learning practitioners.

Play-with-Machine-Learning-Algorithms is a GitHub repository offering the official code for a MOOC course titled "Play with Machine Learning Algorithms" (《Python3 入门机器学习》). This resource serves as a comprehensive companion to the course, providing all source code, updated content, errata information, and additional exercises. Users can download, run, test, and modify the code to gain practical experience with various machine learning algorithms. The repository covers fundamental concepts from machine learning basics to advanced topics like ensemble learning and random forests, making it an invaluable resource for students and practitioners looking to deepen their understanding of machine learning through hands-on coding.

Machine_Learning_Code_Implementation is an open-source GitHub repository offering comprehensive mathematical derivations and pure Python code implementations for a wide array of machine learning algorithms. It is designed to complement classic textbooks like "Statistical Learning Methods" and "Machine Learning," providing practical code examples and theoretical foundations. The repository covers 26 classic algorithms across supervised learning (single and ensemble models), unsupervised learning, and probabilistic models. It aims to help beginners fully grasp algorithm details, implementation methods, and underlying logic, making it an invaluable resource for students and practitioners looking to deepen their understanding of machine learning.