Awesome GitHub RepositoriesEducational Code Notebooks

This project is a beginner coding bootcamp and Python programming curriculum. It provides a structured set of educational materials and exercise files designed to guide students through the Python language from basic to advanced levels. The curriculum is delivered as Jupyter Notebook courseware, combining live code execution with explanatory text for technical demonstrations. It also functions as a project repository, offering a collection of milestone coding exercises and source files for practicing software development and core syntax. The materials are organized into sequential modules an

This is a machine learning educational repository consisting of a collection of notebooks and code examples. It provides practical implementations of diverse machine learning algorithms and workflows, ranging from traditional scientific computing to deep learning. The project features specific implementations of Scikit-Learn models, such as decision trees, random forests, and support vector machines, as well as TensorFlow examples for building neural networks, convolutional layers, and recurrent architectures. It also includes tutorials on reinforcement learning development and the creation o

This project is a community-driven knowledge repository and technical learning resource focused on the field of generative artificial intelligence. It serves as a centralized hub for developers and practitioners to access curated research, tutorials, and foundational concepts necessary for building and deploying modern artificial intelligence applications. The platform distinguishes itself through a collaborative, distributed contribution model that aggregates diverse learning materials into a structured, searchable knowledge base. It covers a wide range of specialized topics, including retri

This project is an educational suite and technical guide designed for mastering video codecs and signal processing. It provides a structured curriculum through an engineering course, interactive labs, and tutorials focused on the fundamental principles of video compression and digital signal processing. The resource includes a technical guide for analyzing specific codecs like AV1, VP9, and H.265. It distinguishes itself by providing a containerized media lab, which ensures a consistent development environment for experimenting with video technology tools and notebooks. The project covers a

This project is a framework and curriculum for self-directed learning, providing a structured methodology for mastering complex technical skills without formal instruction. It combines educational content with a technical study methodology centered on deliberate practice and the psychological habits required for independent mastery. The project is distinguished by its use of interactive notebooks and markdown documentation to deliver a sequenced learning path. It integrates test-driven development patterns into the educational process to provide automated feedback and resolve cognitive barrie

pyprobml is a collection of notebook-based implementations of probabilistic machine learning models and algorithms. It uses scientific computing and data analysis libraries to execute mathematical concepts and theories for practical application and research. The project focuses on the programmatic generation of scientific figures and visualizations to recreate results from a technical text. It employs a system of branch-based asset storage to isolate these generated images from the source code. The repository covers a wide range of probabilistic modeling and machine learning tasks, including

NYU-DLSP20 is a self-paced deep learning course repository that provides a complete educational curriculum covering supervised and unsupervised deep learning fundamentals. The course materials include lecture slides, Jupyter notebooks, and YouTube video recordings, all organized around PyTorch-based code exercises and neural network architecture tutorials. The course is structured as a sequential progression from fundamentals to advanced architectures, with each lecture building on previous material. Assignments are distributed as Jupyter notebooks that students complete and submit, ensuring

Pluto.jl ist eine reaktive Computing-Umgebung für Julia, die als programmierbares Dokumentenformat fungiert. Sie dient als interaktive Data-Science-IDE und polyglottes Computational Notebook, das Julia-Code und Umgebungsabhängigkeiten als versionierbare Quelldateien speichert. Das System zeichnet sich durch sein reaktives Ausführungsmodell aus, das einen gerichteten azyklischen Graphen verwendet, um Variablenabhängigkeiten zu verfolgen und betroffene nachgelagerte Zellen automatisch neu zu berechnen, wenn sich ein Wert ändert. Es stellt Reproduzierbarkeit sicher, indem isolierte Paketumgebungen direkt in die Notebook-Datei integriert werden und Inhalte als reine ausführbare Quelldateien gespeichert werden. Die Plattform deckt ein breites Spektrum an Funktionen ab, einschließlich interaktiver Datenvisualisierung, der Erstellung geführter Bildungsaufgaben und der Generierung statischer Dokumentations-Websites. Sie unterstützt diverse Ausgabemodi wie Slideshow-Präsentationen und Webanwendungen und bietet Tools zum Exportieren von Notebooks in HTML, PDF und reproduzierbarkeitsfokussierte Skripte. Die Umgebung kann über ein Web-Interface auf localhost gestartet oder über Remote-Server und Cloud-Compute-Hosting aufgerufen werden.

This project is a quantum computing educational resource and implementation library. It provides a collection of interactive notebooks and guides designed for learning quantum programming, developing algorithms, and simulating quantum circuits. The resource includes tutorials for implementing standard quantum algorithms and creating custom circuit passes. It specifically covers quantum hardware control, providing instructions on scheduling raw microwave or laser pulses to implement precise gates at the physical layer. The materials cover the broader surface of quantum circuit design, includi