Research & Education

Machine-learning-visualized is an open-source project offering a Jupyter Book filled with Jupyter Notebooks. These notebooks meticulously implement and mathematically derive various machine learning algorithms from first principles, making complex concepts accessible. A key feature includes Interactive Notebooks built with Marimo, allowing users to dynamically observe how weight adjustments impact loss functions. Each notebook's output visualizes the machine learning algorithm's training phase, demonstrating its convergence to optimal weights. The project is structured such that this repository configures and builds the Jupyter Book, while individual machine learning algorithms reside in separate GitHub repositories, which are downloaded via a provided script.

MachineLearningNote is an open-source GitHub repository dedicated to providing comprehensive notes and practical code examples for various machine learning algorithms. Primarily utilizing the Sklearn library in Python, this resource covers a wide array of topics including Logistic Regression, Decision Trees, K-Nearest Neighbors, Naive Bayes, K-Means & DBSCAN, Ensemble Learning, One-Class SVM, PCA, LDA, EM (GMM), SVM, XGBoost, Isolation Forest, Random Forest, LOF, and SVD. Each algorithm is accompanied by detailed explanations and code implementations, often linking to external blog posts for deeper understanding. It serves as an excellent reference for students and practitioners looking to enhance their knowledge and practical skills in machine learning with Python and Sklearn.

MathsDL-spring18 is an open-source repository offering comprehensive materials for the 'Mathematics of Deep Learning' topics course, taught at NYU in Spring 2018. It provides detailed logistics, instructor information, and a full syllabus covering geometric aspects of deep learning, optimization, and generalization. The repository includes lecture slides, references, and outlines for parallel curricula focusing on topics like Dynamic Programming, Policy Learning, and Monte-Carlo Tree Search, with specific readings and questions for each session. This resource is invaluable for students and researchers interested in the theoretical and mathematical foundations of deep learning, offering a structured approach to complex concepts and open problems in the field.

matrixcalc is an open-source GitHub repository hosting the materials for the MIT IAP short course, "Matrix Calculus for Machine Learning and Beyond." Taught by Professors Alan Edelman and Steven G. Johnson, this resource extends traditional calculus to matrix functions and arbitrary vector spaces, crucial for modern applications like machine learning and large-scale optimization. It covers topics such as derivatives as linear operators, multidimensional chain rules, automatic differentiation, and adjoint methods. The course emphasizes matrices as holistic objects and includes practical aspects like numerical computations using the Julia language, making it a valuable resource for those looking to deepen their understanding of advanced calculus in a computational context.

NakedTensor serves as a foundational resource for understanding machine learning concepts within TensorFlow. It presents simplified, bare-bones examples, focusing on fitting straight lines to data through gradient descent. The project is structured to introduce users to TensorFlow's mechanics, starting with a serial processing example, then progressing to tensor operations for parallel computation, and finally demonstrating how to handle large datasets using placeholders and data sampling. This approach makes complex topics like error definition, optimization, and distributed computing accessible, providing a clear pathway for beginners to grasp the core principles of machine learning with TensorFlow.

spark-py-notebooks is a comprehensive collection of IPython/Jupyter notebooks designed to educate users on various Apache Spark concepts using Python (pySpark). The tutorials range from fundamental to advanced topics, focusing on Big Data Analysis and Machine Learning. Users can learn about RDD creation, basic RDD operations like map, filter, and collect, sampling, set operations, and data aggregations. The collection also delves into working with key/value pair RDDs and introduces MLlib for basic statistics, exploratory data analysis, logistic regression, and decision trees. Additionally, it covers Spark SQL for structured processing with DataFrames and includes applications like building a movie recommendation web service.

ttt-rl is a reinforcement learning example implemented in C, designed to teach the basics of reinforcement learning through a tic-tac-toe game. The neural network learns to play against a random adversary from scratch, without any pre-existing knowledge of the game. It uses a simple architecture with a single hidden layer and is contained in under 400 lines of C code, with no external libraries. This project is particularly valuable for programmers, especially young programmers, who want to understand new fields through small, self-contained, and well-commented C programs. It demonstrates how RL can learn complex behaviors from basic reward signals.

BuildingMachineLearningSystemsWithPython is an open-source repository containing the complete source code for the book "Building Machine Learning Systems with Python" by Luis Pedro Coelho and Willi Richert. This resource is invaluable for students, teachers, and professionals looking to understand and implement machine learning systems using Python. The code corresponds to the second edition of the book, published in 2015, and provides practical, hands-on examples for various machine learning concepts. It serves as a direct companion to the book, allowing users to explore, run, and modify the code to deepen their understanding of the topics covered. The repository is hosted on GitHub, making it easily accessible for anyone interested in learning or teaching machine learning with Python.

ciml is an open-source repository offering comprehensive materials for "A Course in Machine Learning." It serves as a valuable resource for both students and educators, providing the full source code for the accompanying book. Beyond the core text, the repository includes a wealth of supplementary course materials such as detailed slides, informative documents, and practical laboratory exercises. This makes ciml an excellent tool for those looking to learn about machine learning through a structured curriculum or for instructors seeking ready-to-use content for their courses. The materials are designed to support a thorough understanding of machine learning concepts.

dmol-book is an open-source project offering a comprehensive book on deep learning for molecules and materials. Hosted on GitHub, this resource allows users to access and build the book locally using Jupyter Book, providing a flexible and customizable learning experience. The repository includes all necessary files and instructions for local setup, making it ideal for researchers and students who want to delve into the intersection of deep learning and scientific applications. It covers various topics relevant to chemistry and materials informatics, serving as a valuable educational tool for those interested in the field.

efficient-dl-systems is an open-source GitHub repository offering comprehensive educational materials for the Efficient Deep Learning Systems course, taught at HSE University and Yandex School of Data Analysis. The repository includes a detailed syllabus, lecture notes, and seminar materials covering a wide range of topics, from foundational GPU architecture and CUDA API to advanced concepts like distributed training, large model optimization, and inference algorithms. It provides practical insights into performance measurement, mixed-precision training, data-parallel techniques, and deployment of deep learning models. The course content is structured week-by-week, making it an invaluable resource for students and researchers looking to deepen their understanding of efficient deep learning practices.

feature-engineering-book is the official GitHub code repository accompanying the book "Feature Engineering for Machine Learning" by Alice Zheng and Amanda Casari, published by O'Reilly in 2018. This resource is invaluable for students, researchers, and practitioners looking to implement the feature engineering techniques discussed in the book. The repository contains various Jupyter Notebooks covering topics such as binning, count features, log and Box-Cox transformations, interaction features, text processing (TF-IDF, chunking), regression on categorical variables, feature hashing, PCA, K-means clustering for featurization, and HOG image features. It also includes end-to-end recommender system examples, providing practical code for a deeper understanding of machine learning concepts.

AI Song Creator: Musicraft is an Android mobile application designed to simplify music creation through artificial intelligence. Users can generate original, studio-quality music by providing text prompts, lyrics, or even images, which the AI then transforms into complete songs, instrumental tracks, or beats. The app supports a variety of genres, making it a versatile tool for different creative needs. It is ideal for content creators, musicians, and anyone looking for royalty-free background music or a source of creative inspiration. The intuitive interface aims to make music generation accessible to users of all skill levels, enabling quick and efficient production of unique audio content.

Kaggle-titanic is an open-source tutorial designed for individuals interested in data analytics and using Python for Kaggle's Data Science competitions, specifically the Titanic Machine Learning From Disaster challenge. The tutorial, presented as an IPython Notebook, guides users through essential data science practices including importing and cleaning data with Pandas, exploring data through visualizations with Matplotlib, and performing data analysis. It also covers supervised machine learning techniques such as Logit Regression, Support Vector Machines (SVM) with multiple kernels, and Basic Random Forest. The resource further demonstrates K-folds cross-validation for evaluating results locally and outputting them for Kaggle. This comprehensive guide is ideal for beginners looking to gain practical experience in data science and machine learning.

MML-Book is an open-source repository offering comprehensive code and solutions for the "Mathematics for Machine Learning" (MML) book. This resource is specifically designed to aid self-study, providing Python code examples that help users better understand various machine learning concepts. It includes detailed solutions to exercises for each chapter, with notebooks that render LaTeX for clear mathematical explanations. The repository covers topics from Chapter 2 through Chapter 7, with a focus on practical application and conceptual clarity. It's a valuable asset for anyone looking to deepen their understanding of the mathematical foundations of machine learning through hands-on practice and guided solutions.

Mindojo is an innovative adaptive e-learning platform designed to instill knowledge effectively and affordably. It functions as an AI private tutor, engaging students through personalized dialogues and adapting to their individual learning styles. The platform builds a robust model of each student’s mind, using sophisticated algorithms to predict the most efficient teaching interactions. Mindojo offers intuitive and powerful authoring tools, enabling users to model course knowledge, compose interactive lessons, and collaborate. It's versatile, suitable for standalone commercial products, in-house training, university course supplements, or flipped classrooms. Mindojo currently powers successful prep courses for exams like GMAT and CFA, demonstrating its capability to significantly improve student outcomes.

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.

AAAMLP-CN is the Chinese translated version of Abhishek Thakur's influential article, "Approaching (Almost) Any Machine Learning Problem." This resource provides a comprehensive guide to building an automated machine learning framework, originally published on LinkedIn. The project offers an online reading website and an EPUB version for convenient access. It includes completed translations, corrections for textual and code errors, and future plans to analyze excellent solutions from Kaggle Playground series competitions. The translation covers key topics such as supervised and unsupervised learning, cross-validation, evaluation metrics, feature engineering, hyperparameter optimization, and various classification and regression methods.

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.

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.

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.

AnyQuestions.ai is an AI-powered education platform designed to enhance the learning experience for students and educators. Users can upload various documents, and the platform leverages AI to generate comprehensive answers, complete with citations for accuracy and reliability. Beyond just answering questions, AnyQuestions.ai also creates AI-generated flashcards, interactive learning maps, and custom quizzes. These features are specifically designed to optimize study habits, reinforce understanding, and provide personalized learning paths, making it a versatile tool for both self-study and educational content creation.