Awesome GitHub RepositoriesDeep Learning Framework Implementations

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

Mamba is a deep learning framework designed for building and training sequence models that process long-range data dependencies with linear-time computational efficiency. By utilizing selective state space modeling, the library enables the construction of neural network architectures that replace traditional attention mechanisms with high-performance state space operations. The framework distinguishes itself through the use of data-dependent state gating, which allows the model to dynamically filter information flow based on the input sequence. To ensure high throughput, it incorporates hardw

FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated trading strategies. It functions as a quantitative finance toolkit that integrates deep learning algorithms with financial market simulations to address complex portfolio management and asset allocation tasks. The platform provides an end-to-end pipeline for transforming raw market data into actionable trading models. The project distinguishes itself through a layered, modular architecture that separates data processing, environment simulation, and agent training. This design allow

Open Llama is an open source large language model and pre-trained transformer designed as a permissively licensed alternative to proprietary weights. It serves as a base model reproduction of the Llama architecture, providing a set of weights for a decoder-only transformer. The project provides a transparently trained model based on the RedPajama dataset, supporting unrestricted commercial and research use. It includes systems for serving pre-trained weights in various sizes. The project covers natural language processing research and performance benchmarking through text quality evaluation

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

tiny-dnn is a header-only C++14 deep learning framework for building, training, and running inference on neural networks. It constructs static computational graphs at compile time using template-based layer composition, with a gradient-based backpropagation engine and minibatch stochastic gradient descent for training, all without external dependencies beyond the C++14 standard library. The framework supports importing pre-trained models from the Caffe framework directly, parsing its binary serialization format without requiring external protocol buffer libraries. It provides CPU-optimized te

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

Vim is a computer vision framework and deep learning research tool designed for image classification and visual representation learning. It implements a vision-specific state space model that processes image data by converting two-dimensional grids into one-dimensional sequences, enabling the extraction of spatial features through bidirectional sequence modeling. The architecture distinguishes itself by replacing traditional quadratic attention mechanisms with state space operations that scale linearly with input length. It utilizes a selective gating mechanism to dynamically filter visual in

Chips est un framework de bibliothèque C modulaire, header-only, conçu pour construire des simulateurs matériels cycle-accurate et répliquer des architectures informatiques huit bits historiques. Il fournit les composants fondamentaux nécessaires pour construire des systèmes vintage complets en intégrant des microprocesseurs émulés individuels et des puces périphériques. Le framework se distingue par une architecture basée sur des composants où les modules matériels sont implémentés sous forme d'en-têtes autonomes qui peuvent être câblés ensemble pour former des systèmes complexes. Il modélise les interactions matérielles à un bas niveau, utilisant l'émulation de signal au niveau des broches et la communication par bus mappé en mémoire pour garantir un comportement déterministe. Les développeurs peuvent capturer l'état interne complet d'un émulateur dans un tampon mémoire, permettant des instantanés persistants et une restauration d'état précise. Le projet inclut une suite complète d'outils de diagnostic et de développement, tels que la visualisation de débogage en mode immédiat et la surveillance en temps réel des registres système et des framebuffers. Il fournit également des utilitaires en ligne de commande pour automatiser la génération de définitions de composants matériels et de code source, facilitant la construction d'environnements embarqués personnalisés.