Projects sharing features with Lihang Code

This repository is a structured educational archive of classic computer science algorithms and data structures implemented in Python. It serves as a reference library designed for study and technical skill development, providing clean, readable examples of fundamental computational techniques rather than production-ready software components. The project distinguishes itself through its idiomatic approach, utilizing native language features and standard library conventions to demonstrate algorithmic logic clearly. Each implementation is organized into a hierarchical directory structure that mi

This project is a machine learning educational resource and implementation guide for Python. It provides a collection of executable code and notebooks that demonstrate predictive modeling, data analysis workflows, and the implementation of various machine learning algorithms. The repository features practical examples of classification, regression, and clustering tasks using Scikit-Learn, alongside tutorials for building and training deep learning architectures with TensorFlow. These include implementations of convolutional and recurrent networks. The content covers a broad range of capabili

This project is a machine learning study guide and technical knowledge base. It serves as a version-controlled repository of mathematical formulas and algorithmic explanations, providing instructional material and reference notes for the study of artificial intelligence. The content is structured as a markdown-based knowledge base that pairs theoretical mathematical explanations directly with code implementations. This approach demonstrates model mechanics in practice across several specialized domains, including deep learning research, probabilistic graphical modeling, and reinforcement lear

This project is a deep learning implementation library and neural network theory repository. It translates mathematical derivations from textbooks and literature into functional Python code to demonstrate how deep learning algorithms work. The codebase focuses on low-level algorithm implementation by using numerical libraries instead of high-level deep learning frameworks. This approach maps theoretical mathematical proofs to executable functions to verify principles and expose the underlying arithmetic and data flow of neural networks. The project covers the implementation of deep learning

This repository serves as an educational collection of interactive notebooks and code examples designed to demonstrate fundamental machine learning and deep learning concepts. It provides a structured environment for exploring data science workflows, ranging from basic numerical computing and statistical analysis to the construction of complex neural network architectures. The project distinguishes itself through a focus on hands-on experimentation, offering practical implementations for tasks such as computer vision, natural language processing, and statistical simulation. Users can engage w

ML-foundations is a machine learning educational curriculum and computer science study guide. It provides a structured learning path focused on the mathematical foundations and computational prerequisites required for studying machine learning. The project serves as a Python mathematics course, delivering interactive notebooks and coding exercises to teach linear algebra, calculus, and statistics. It translates abstract mathematical formulas into concrete algorithmic code to help learners understand the principles underpinning machine learning algorithms. The curriculum covers data science p

This project is a collection of supervised and unsupervised machine learning algorithms implemented from scratch using Python. It serves as an educational resource for studying model training, parameter optimization, and the implementation of core predictive models. The library provides a variety of supervised learning tools, including linear and logistic regression, decision trees, and support vector machines. It also features unsupervised learning capabilities for discovering patterns in unlabeled datasets through clustering algorithms. Broad capability areas include ensemble learning thro

This project is an educational collection of interactive Jupyter notebooks designed to illustrate fundamental machine learning algorithms and mathematical principles. It serves as a resource for bridging the gap between abstract equations and practical implementation through a combination of narrative text and executable code. The collection utilizes a modular architecture where individual algorithm implementations are isolated to facilitate independent study. It incorporates both interactive code examples and static graphical assets to represent complex statistical concepts and model behavio

This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg

This project is an educational resource providing a mathematical foundation in probability and statistics for machine learning. It offers a collection of interactive notebooks and textbooks designed to explain core statistical theories and data science principles through practical code examples. The content is structured into modular chapters that allow for self-paced learning of topics such as Bayesian inference and probability distributions. By utilizing browser-based execution and declarative visualization, the project enables users to manipulate variables and observe mathematical outcomes

This project is a technical curriculum and learning path for machine learning, providing a structured sequence of mathematical foundations, core concepts, and professional workflows. It serves as a comprehensive guide and resource index that connects theoretical principles to the specific software libraries and tools used in real-world implementation. The repository functions as a project workflow blueprint, outlining the sequential steps required to solve machine learning problems from initial discovery through to final deployment. It maps theoretical mathematical principles to practical app

This repository provides a collection of machine learning algorithms implemented from scratch using pure Python. It serves as an educational resource designed to demonstrate the internal logic and mathematical foundations of predictive models without relying on external machine learning frameworks or black-box libraries. The project distinguishes itself by mapping code implementations directly to their underlying statistical and calculus-based formulas. Each model is constructed using base language primitives and manual gradient descent optimization, allowing users to observe the mechanics of

This is the companion code repository for the third edition of the book Python Machine Learning. It delivers the entire learning path as a structured collection of Jupyter notebooks that progress from classical machine learning algorithms to advanced deep learning models, with every concept demonstrated through executable code and narrative text. What distinguishes this resource is its pedagogical design. Each notebook cell encapsulates a single conceptual step, letting readers run, inspect, and modify discrete units of learning. The code provides interchangeable implementations of deep lea

This project is a machine learning coursework repository containing a collection of Python exercises and notebooks. It is designed for implementing foundational machine learning algorithms and completing curriculum assignments through interactive documents that combine instructional text and executable code. The repository provides code formatted for compatibility with automated grading systems, allowing for the submission and validation of technical exercises. It includes predefined environment configurations and dependency locks to ensure consistent execution of data science tools across di

This project is a structured AI engineering curriculum and educational program designed to teach the construction of machine learning models, neural networks, and autonomous agents from the ground up. It serves as a comprehensive machine learning course covering mathematical foundations, deep learning architectures, and reinforcement learning through practical implementation. The project provides a technical framework for building autonomous loops and memory systems via an agent framework, as well as guides for implementing multimodal AI systems that integrate vision, audio, and text processi

This project is a machine learning curriculum and educational course repository designed as a structured three-month study plan. It provides a guided path for mastering data science and artificial intelligence using the Python programming language. The repository organizes learning materials and code examples to cover mathematics, algorithms, and deep learning fundamentals. It uses a modular curriculum structure to break the domain into discrete monthly and weekly segments. The project functions as a curated resource map that aligns source code and notes with external instructional videos an

This project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem. The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ

ML for Hackers is a machine learning educational resource and library designed for learning the fundamentals of algorithmic programming and data analysis. It provides a neural network framework and a collection of mathematical implementations for building and training predictive models. The project utilizes a modular architecture for stacking linear transformations and activation layers. It implements core deep learning components from scratch using multi-dimensional arrays for tensor algebra and operations. The framework covers a variety of algorithmic capabilities, including automatic diff

This project is a digital collection of academic material on deep learning provided as a machine learning educational resource. It delivers the complete textbook and individual chapters in portable document format for offline study and research. The repository includes electronic publication versions of the textbooks optimized for digital reading devices and e-book readers. It functions as a segmented document repository, providing the text both as a full volume and split into individual chapters to allow for targeted reading.

Ai-Learn is an educational repository and technical reference designed to facilitate the mastery of artificial intelligence and data science workflows. It provides a structured curriculum that combines theoretical mathematical foundations with practical coding exercises, enabling users to build predictive models, neural networks, and analytical pipelines using Python. The project distinguishes itself by emphasizing a first-principles approach to machine learning. Rather than relying solely on high-level abstractions, it guides users through the reconstruction of core algorithms from scratch,

This project is a machine learning educational repository providing a collection of implementations and guides for machine learning and deep learning algorithms. It serves as a deep learning model library and a reference for training workflows, covering foundational machine learning, convolutional, recurrent, and transformer architectures. The collection includes a generative adversarial network suite for synthesizing realistic images and performing image-to-image translation. It also functions as a computer vision implementation guide for object detection and semantic segmentation, alongside

This repository serves as a structured educational resource for machine learning and data science, providing a centralized collection of tutorials, lecture notes, and implementation guides. It is designed to support self-directed learning by organizing complex technical concepts into a clear, hierarchical path that spans from foundational statistical methods to advanced deep learning architectures. The project distinguishes itself through a comprehensive approach to skill development, bridging the gap between theoretical algorithmic foundations and functional software applications. It offers

This project is a collection of reference implementations for algorithms, mathematics, cryptography, compression, and machine learning written in C#. It serves as an educational library providing standard implementations of sorting, searching, and graph theory algorithms. The repository covers a wide range of computational domains, including combinatorial optimization for constraint satisfaction and scheduling, as well as symmetric and classical cryptographic ciphers. It also provides reference code for lossless data compression techniques and fundamental machine learning primitives such as r

This repository contains the digital textbook and supplementary materials for probabilistic machine learning education. It provides structured text and guided study materials covering the mathematical foundations of probability and neural networks. The project emphasizes reproducibility through a collection of interactive notebooks and standalone scripts used to recreate data plots and figures from the text. These materials are hosted in external environments to allow users to execute complex machine learning code without local installation. The educational surface includes lecture slides, e

This project is a comprehensive, community-driven knowledge repository that serves as a centralized hub for data science resources. It provides a structured index of educational materials, software packages, and professional development tools designed to support both students and practitioners in navigating the data science landscape. The repository distinguishes itself through a hierarchical taxonomy that organizes a vast collection of external links into a human-readable, markdown-based document. By relying on distributed contributions, the project maintains an up-to-date snapshot of the fi

This repository serves as an educational collection of Python implementations for fundamental machine learning algorithms and statistical models. It provides a structured environment for learning core concepts through interactive computational documents that combine live code, narrative text, and data visualizations. The codebase focuses on predictive modeling development, offering instructional examples for building and evaluating regression, classification, and neural network models. It utilizes standardized data science library interfaces to demonstrate how to implement and execute these a

This project serves as an educational and practical resource for mastering machine learning workflows using Python. It provides a comprehensive collection of code examples and exercises designed to guide users through the implementation of predictive systems, ranging from fundamental algorithms to deep learning architectures. The repository distinguishes itself by offering a structured approach to both classical machine learning and neural network training. It covers the full lifecycle of model development, including the orchestration of reusable data transformation pipelines, advanced ensemb