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 state space model vision framework designed for image classification and visual representation learning. It functions as a computer vision research tool that converts two-dimensional image grids into one-dimensional sequences to extract spatial features. The system implements a linear-scaling image classifier that replaces quadratic attention mechanisms with state space operations. This approach utilizes bidirectional sequence modeling and selective gating mechanisms to process visual data. The framework covers computer vision benchmarking and image classification research, providin

Chips साइकिल-सटीक हार्डवेयर सिमुलेटर बनाने और ऐतिहासिक आठ-बिट कंप्यूटिंग आर्किटेक्चर को दोहराने के लिए डिज़ाइन किया गया एक मॉड्यूलर, हेडर-ओनली C लाइब्रेरी फ्रेमवर्क है। यह व्यक्तिगत एमुलेटेड माइक्रोप्रोसेसरों और परिधीय चिप्स को इंटीग्रेट करके पूर्ण विंटेज सिस्टम बनाने के लिए आवश्यक आधारभूत घटक प्रदान करती है। फ्रेमवर्क एक घटक-आधारित आर्किटेक्चर के माध्यम से खुद को अलग करती है जहां हार्डवेयर मॉड्यूल को स्टैंडअलोन हेडर के रूप में कार्यान्वित किया जाता है जिन्हें जटिल सिस्टम बनाने के लिए एक साथ जोड़ा जा सकता है। यह हार्डवेयर इंटरैक्शन को निम्न स्तर पर मॉडल करती है, पिन-स्तरीय सिग्नल एमुलेशन और मेमोरी-मैप्ड बस संचार का उपयोग करके नियतात्मक व्यवहार सुनिश्चित करती है। डेवलपर्स एक एमुलेटर की पूरी आंतरिक स्थिति को मेमोरी बफर में कैप्चर कर सकते हैं, जो पर्सिस्टेंट स्नैपशॉट और सटीक स्थिति बहाली को सक्षम बनाता है। प्रोजेक्ट में नैदानिक और डेवलपमेंट टूल का एक व्यापक सूट शामिल है, जैसे इमीडिएट-मोड डिबग विज़ुअलाइज़ेशन और सिस्टम रजिस्टरों और फ्रेमबफ़र्स की रीयल-टाइम निगरानी। यह हार्डवेयर घटक परिभाषाओं और स्रोत कोड के निर्माण को ऑटोमेट करने के लिए कमांड-लाइन उपयोगिताएँ भी प्रदान करती है, जो कस्टम एम्बेडेड वातावरण के निर्माण की सुविधा प्रदान करती है।