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
MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a comprehensive environment for executing, optimizing, and deploying neural network models directly on mobile and resource-constrained edge devices. The framework distinguishes itself through a robust model optimization toolkit that supports quantization, compression, and structural graph manipulation to minimize memory footprint and maximize execution speed. It features a modular architecture that abstracts hardware-specific backends, allowing models to run efficiently across diverse
This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex
This project is an algorithm template library and coding interview study guide providing reusable code patterns for common data structures and algorithms. It serves as a reference for optimized strategies and a structured learning path to build proficiency in algorithmic problem solving and competitive programming. The library focuses on standardized implementations of key algorithmic patterns, including sliding windows, backtracking, dynamic programming, and binary search. It provides specific templates for managing binary search trees, searching rotated sorted arrays, and executing divide-a
This project is a mathematics programming pattern library and translation guide designed to map academic mathematical symbols and formulas into programmable logic. It serves as a reference for converting complex notations into software implementations.
Principalele funcționalități ale experience-monks/math-as-code sunt: Formula-to-Code Translation Guides, Symbol-to-Logic Mappings, Logical Set Mappings, Set Theory Logic Mappings, Logic Expression Mappings, Algorithmic Notation Translations, Calculus Logic Translations, Formula Implementation Guides.
Alternativele open-source pentru experience-monks/math-as-code includ: mingchaozhu/deeplearning — This project is a deep learning implementation library and neural network theory repository. It translates… alibaba/mnn — MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a… d2l-ai/d2l-en — This project is an educational platform and research toolkit designed to teach deep learning through a combination of… greyireland/algorithm-pattern — This project is an algorithm template library and coding interview study guide providing reusable code patterns for… deckarep/golang-set — This project is a set theory library for Go that provides a data structure for storing unique elements of any… connorferster/handcalcs — handcalcs is a mathematical documentation generator and Python LaTeX calculation renderer. It serves as an automated…