Search Results for "deep learning" - Page 10
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Showing 659 open source projects for "deep learning"
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TurboQuant PyTorch
From-scratch PyTorch implementation of Google's TurboQuant
TurboQuant PyTorch is a specialized deep learning optimization framework designed to accelerate neural network inference and training through advanced quantization techniques within the PyTorch ecosystem. The project focuses on reducing the computational and memory footprint of models by converting floating-point representations into lower-precision formats while preserving performance. -
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Nvitop
An interactive NVIDIA-GPU process viewer and beyond
...As a resource monitor, it includes many features and options, such as tree-view, environment variable viewing, process filtering, process metrics monitoring, etc. Beyond that, the package also ships a CUDA device selection tool nvisel for deep learning researchers. It also provides handy APIs that allow developers to write their own monitoring tools. -
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Linux insides
A book-in-progress about the Linux kernel and its insides
...It is written for readers who already have some familiarity with C and assembly language and want to understand what happens under the hood of Linux. The material is continuously updated as the kernel evolves, reflecting changes in modern kernel versions. Overall, linux-insides is widely regarded as a deep technical learning resource for systems programmers and advanced Linux enthusiasts. -
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Aim
An easy-to-use & supercharged open-source experiment tracker
Aim logs all your AI metadata (experiments, prompts, etc) enabling a UI to compare & observe them and SDK to query them programmatically. The Aim standard package comes with all integrations. If you'd like to modify the integration and make it custom, create a new integration package and share with others. Aim is an open-source, self-hosted AI Metadata tracking tool designed to handle 100,000s of tracked metadata sequences. The two most famous AI metadata applications are: experiment... -
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AlphaFold 3
AlphaFold 3 inference pipeline
AlphaFold 3, developed by Google DeepMind, is an advanced deep learning system for predicting biomolecular structures and interactions with exceptional accuracy. This repository provides the complete inference pipeline for running AlphaFold 3, though access to the model parameters is restricted and must be obtained directly from Google under specific terms of use. The system is designed for scientific research applications in structural biology, biochemistry, and bioinformatics, enabling accurate modeling of proteins, ligands, and covalent modifications. ... -
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Agent Reinforcement Trainer
Train multi-step agents for real-world tasks using GRPO
Agent Reinforcement Trainer, or ART is an open-source reinforcement learning framework tailored to training large language model agents through experience, making them more reliable and performant on multi-turn, multi-step tasks. Instead of just manually crafting prompts or relying on supervised fine-tuning, ART uses techniques like Group Relative Policy Optimization (GRPO) to let agents learn from environmental feedback and reward signals. -
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PaddleOCR-json
OCR offline image text recognition command line windows program
...It wraps the PaddleOCR models, which are capable of detecting and recognizing text in a wide variety of languages and layouts, into a self-contained executable that can be run locally without needing a deep learning environment configured manually. This makes it practical for developers or system integrators who want reliable OCR output in JSON while avoiding the complexity of training or managing models by hand. Projects and wrappers built around PaddleOCR-json demonstrate how it can be integrated into other applications, such as desktop OCR utilities or language-specific bindings, because the JSON output is easy to parse and consume. -
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Real-Time Voice Cloning
Clone a voice in 5 seconds to generate arbitrary speech in real-time
Real-Time Voice Cloning is an influential deep-learning repository that demonstrates how to clone a voice from just a few seconds of audio and then generate arbitrary speech in that voice in near real time. It implements the SV2TTS pipeline (“Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis”) in three stages: a speaker encoder, a synthesizer, and a vocoder. -
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NNCF
Neural Network Compression Framework for enhanced OpenVINO
NNCF (Neural Network Compression Framework) is an optimization toolkit for deep learning models, designed to apply quantization, pruning, and other techniques to improve inference efficiency. -
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NeuralOperators.jl
DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia
Neural operator is a novel deep learning architecture. It learns an operator, which is a mapping between infinite-dimensional function spaces. It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). -
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Megatron-LM
Ongoing research training transformer models at scale
Megatron-LM is a GPU-optimized deep learning framework from NVIDIA designed to train extremely large transformer-based language models efficiently at scale. The repository provides both a reference training implementation and Megatron Core, a composable library of high-performance building blocks for custom large-model pipelines. It supports advanced parallelism strategies including tensor, pipeline, data, expert, and context parallelism, enabling training across massive multi-GPU and multi-node clusters. ... -
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FinRobot
An Open-Source AI Agent Platform for Financial Analysis using LLMs
...It provides developers and quants with structured modules to fetch market data, process time series, generate technical indicators, and construct features appropriate for machine learning models, while also supporting backtesting and evaluation metrics to measure strategy performance. Built with modularity in mind, FinRobot allows users to plug in custom models — from classical algorithms to deep learning architectures — and orchestrate components in pipelines that can run reproducibly across experiments. The framework also tends to include automation layers for deployment, enabling trained models to operate in live or simulated environments with scheduled re-training and risk controls in place. -
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OSMO
The developer-first platform for scaling complex Physical AI workloads
OSMO is a developer-first orchestration platform designed to scale complex physical AI workflows across heterogeneous computing environments, including cloud GPUs, simulation clusters, and edge devices. It was originally built internally at NVIDIA to support robotics and embodied AI systems, where workflows span multiple stages such as data generation, training, simulation, and hardware testing. The platform addresses what NVIDIA refers to as the “three computer problem” by unifying these... -
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DeepProve
Framework to prove inference of ML models blazingly fast
DeepProve is an advanced cryptographic framework designed to verify machine learning model inference using zero-knowledge proofs, enabling trustless validation of AI computations without exposing underlying data. The project focuses on zkML, a rapidly emerging field that combines machine learning with zero-knowledge cryptography to ensure both privacy and correctness. It supports neural network architectures such as multilayer perceptrons and convolutional neural networks, allowing developers to prove that a model’s output is correct without revealing inputs or model details. deep-prove leverages advanced proof systems such as sumcheck protocols and GKR-based constructions to achieve significantly faster proving times compared to earlier approaches. ... -
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System Design
Learn how to design systems and prepare for system design interviews
This project is an open, course-style repository designed to help you learn system design from fundamentals through advanced, interview-ready thinking. It organizes core networking and distributed-systems concepts into a structured path, so you can build intuition before jumping into “design X” exercises. It covers the building blocks that show up in real architectures, such as DNS, load balancing, caching, CDNs, proxies, scalability and availability tradeoffs, and storage patterns, then... -
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming. Easy model building using flexible encoder-decoder architecture. Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more. GPU-friendly test-time augmentation TTA for segmentation and classification. GPU-friendly inference on huge (5000x5000) images. Every-day common routines (fix/restore random seed, filesystem utils, metrics). Losses:... -
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Antigravity Awesome Skills
The Ultimate Collection of 700+ Agentic Skills for Claude Code
Antigravity Awesome Skills is a playful yet practical repository that curates a set of clever, expressive, and sometimes whimsical AI agent skill templates designed to help users bootstrap agent behavior quickly. Rather than focusing on production-grade systems, it provides creative and high-impact skills that demonstrate how agents can be used to automate tasks, generate content, assist with daily operations, or integrate into larger workflows with minimal configuration. The project... -
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HunyuanVideo
HunyuanVideo: A Systematic Framework For Large Video Generation Model
HunyuanVideo is a cutting-edge framework designed for large-scale video generation, leveraging advanced AI techniques to synthesize videos from various inputs. It is implemented in PyTorch, providing pre-trained model weights and inference code for efficient deployment. The framework aims to push the boundaries of video generation quality, incorporating multiple innovative approaches to improve the realism and coherence of the generated content. Release of FP8 model weights to reduce GPU... -
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Deep-Learning-Interview-Book
Interview guide for machine learning, mathematics, and deep learning
Deep-Learning-Interview-Book collects structured notes, Q&A, and concept summaries tailored to deep-learning interviews, turning scattered study into a coherent playbook. It spans the core math (linear algebra, probability, optimization) and the practitioner topics candidates actually face, like CNNs, RNNs/Transformers, attention, regularization, and training tricks. -
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Diplomacy Cicero
Code for Cicero, an AI agent that plays the game of Diplomacy
The project is the codebase for an AI agent named Cicero developed by Facebook Research. It is designed to play the board game Diplomacy by combining open-domain natural language negotiation with strategic planning. The repository includes training code, model checkpoints, and infrastructure for both language modelling (via the ParlAI framework) and reinforcement learning for strategy agents. It supports two variants: Cicero (which handles full “press” negotiation) and Diplodocus (a variant... -
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dLLM
dLLM: Simple Diffusion Language Modeling
...The project provides an integrated pipeline that standardizes how diffusion language models are trained, evaluated, and deployed, helping researchers reproduce experiments and compare results more easily. The framework includes scalable training infrastructure inspired by modern deep learning toolkits and supports integrations with widely used libraries for distributed training. -
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Transcoder
Hardware-accelerated video transcoding using Android MediaCodec APIs
...Unlike traditional speech translation systems that rely on multi-stage pipelines, Transcoder directly translates one speaker’s video into another language while preserving facial expressions, lip-sync, and vocal identity. Designed for real-time use and production-grade pipelines, Transcoder combines advanced deep learning models with GPU acceleration to deliver high-quality translations across languages. It’s built with researchers and developers in mind, offering tools for testing, evaluating, and deploying AI-driven media localization. -
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Clarity AI Upscaler
AI Image Upscaler & Enhancer
Clarity AI Upscaler is an open-source AI image enhancement tool designed to increase the resolution and visual quality of images using modern generative techniques. The system uses deep learning models based on diffusion and other image generation methods to reconstruct high-resolution versions of low-resolution images while preserving important visual details. Unlike traditional interpolation-based upscaling algorithms, the system generates additional visual information that improves perceived clarity and sharpness. ... -
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Saeba's Blog
Where Saeba writes his blog, he plans to write four series: JavaScript
Blog is a large Chinese-language frontend engineering knowledge base maintained by mqyqingfeng. It collects in-depth articles on JavaScript internals, practical JavaScript topics, ES6, React, and broader frontend development ideas. Rather than being a software package, it functions as a structured educational repository for developers who want to understand how JavaScript features work under the hood. Many articles focus on concepts such as execution context, scope, prototypes, closures,... -
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JUDI.jl
Julia Devito inversion
...Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling operators can also be used as layers in (convolutional) neural networks to implement physics-augmented deep learning algorithms thanks to its implementation of ChainRules's rrule for the linear operators representing the discre wave equation.
