Search Results for "deep learning" - Page 9
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Showing 630 open source projects for "deep learning"
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FLAML
A fast library for AutoML and tuning
FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classical machine learning models and deep neural networks. -
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micrograd
A tiny scalar-valued autograd engine and a neural net library
...The repository is intentionally compact and readable, prioritizing clarity over performance so learners can follow every step of gradient flow and parameter updates. It is commonly used as a learning bridge between basic calculus intuition and full-scale deep learning frameworks, helping developers understand why autodiff libraries behave the way they do. -
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theByteBook
In-depth explanation of cloud native related technologies
theByteBook is a large open-source repository that publishes a comprehensive technical book focused on high-availability system design, modern cloud-native infrastructure, and foundational engineering concepts, serving as both a learning resource and architecture reference. The content covers deep dives into networking principles, container ecosystems, Kubernetes, service meshes, distributed systems, and SRE/DevOps practices, aiming to help practitioners build reliable, scalable, and cost-efficient systems. Although originally authored in Chinese and tied to a published physical book, the repository hosts the full text as markdown and site content, letting developers read versioned chapters online or build a local copy for offline study. ... -
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cracking-the-data-science-interview
A Collection of Cheatsheets, Books, Questions, and Portfolio
Cracking the Data Science Interview is an open educational repository that collects study materials, resources, and reference links for preparing for data science interviews. The project organizes content across many fundamental areas of data science, including statistics, probability, SQL, machine learning, and deep learning. It includes cheat sheets that summarize important technical concepts commonly discussed during technical interviews. The repository also provides links to recommended books, tutorials, practice platforms, and blog posts that help learners strengthen their theoretical and practical skills. In addition to conceptual study materials, the project includes interview question banks and case study prompts that simulate real hiring scenarios. ... -
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Raster Vision
Open source framework for deep learning satellite and aerial imagery
Raster Vision is an open source framework for Python developers building computer vision models on satellite, aerial, and other large imagery sets (including oblique drone imagery). There is built-in support for chip classification, object detection, and semantic segmentation using PyTorch. Raster Vision allows engineers to quickly and repeatably configure pipelines that go through core components of a machine learning workflow: analyzing training data, creating training chips, training... -
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PySR
High-Performance Symbolic Regression in Python and Julia
...Here, one essentially uses symbolic regression to convert a neural net to an analytic equation. Thus, these tools simultaneously present an explicit and powerful way to interpret deep neural networks. -
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Tile Kernels
A kernel library written in tilelang
...TileKernels also includes testing and benchmarking utilities to help evaluate correctness and performance. Its main value is providing reusable TileLang-based kernels for experimental and production-adjacent deep-learning systems. -
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TimeMixer
Decomposable Multiscale Mixing for Time Series Forecasting
TimeMixer is a deep learning framework designed for advanced time series forecasting and analysis using a multiscale neural architecture. The model focuses on decomposing time series data into multiple temporal scales in order to capture both short-term seasonal patterns and long-term trends. Instead of relying on traditional recurrent or transformer-based architectures, TimeMixer is implemented as a fully multilayer perceptron–based model that performs temporal mixing across different resolutions of the data. ... -
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Advanced AI explainability for PyTorch
Advanced AI Explainability for computer vision
pytorch-grad-cam is an open-source library that provides advanced explainable AI techniques for interpreting the predictions of deep learning models used in computer vision. The project implements Grad-CAM and several related visualization methods that highlight the regions of an image that most strongly influence a neural network’s decision. These visualization techniques allow developers and researchers to better understand how convolutional neural networks and transformer-based vision models make predictions. ... -
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Roadmap To Learn Generative AI In 2025
Basic Machine Learning Natural Language Processing Roadmap
...The roadmap outlines recommended topics, sequential steps, and associated resources (tutorials, notebooks, project ideas) to build competence in generative modeling from conceptual understanding to implementation and deployment. By organizing the learning journey in digestible phases — from fundamentals of neural networks to deep generative architectures, and from model training to serving/inference pipelines — it reduces the cognitive load of “where to start”. -
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Learn AI Engineering
Learn AI and LLMs from scratch using free resources
Learn AI Engineering is a learning path for AI engineering that consolidates high-quality, free resources across the full stack: math, Python foundations, machine learning, deep learning, LLMs, agents, tooling, and deployment. Rather than a loose bookmark list, it organizes topics into a progression so learners can start from fundamentals and move toward practical, production-oriented skills. -
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Optax
Optax is a gradient processing and optimization library for JAX
Optax is a gradient processing and optimization library for JAX. It is designed to facilitate research by providing building blocks that can be recombined in custom ways in order to optimize parametric models such as, but not limited to, deep neural networks. We favor focusing on small composable building blocks that can be effectively combined into custom solutions. Others may build upon these basic components in more complicated abstractions. Whenever reasonable, implementations prioritize... -
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React Native ExecuTorch
Declarative way to run AI models in React Native on device
...The project supports a range of AI use cases, including large language models, computer vision, OCR, object detection, speech processing, segmentation, and embeddings. It helps React Native developers use local AI capabilities without needing deep native programming or machine learning infrastructure expertise. The library is especially relevant for privacy-first apps, offline experiences, and mobile products that need low-latency inference. Its main value is bridging React Native with native AI execution so apps can run powerful models directly on user devices. -
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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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HY-Motion 1.0
HY-Motion model for 3D character animation generation
HY-Motion 1.0 is an open-source, large-scale AI model suite developed by Tencent’s Hunyuan team that generates high-quality 3D human motion from simple text prompts, enabling the automatic production of fluid, diverse, and semantically accurate animations without manual keyframing or rigging. Built on advanced deep learning architectures that combine Diffusion Transformer (DiT) and flow matching techniques, HY-Motion scales these approaches to the billion-parameter level, resulting in strong instruction-following capabilities and richer motion outputs compared to existing open-source models. The training strategy for the HY-Motion series includes extensive pre-training on thousands of hours of varied motion data, fine-tuning on curated high-quality datasets, and reinforcement learning with human feedback, which improves both the plausibility and adaptability of generated motion sequences. -
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Koila
Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code
Koila is a lightweight Python library designed to help developers avoid memory errors when training deep learning models with PyTorch. The library introduces a lazy evaluation mechanism that delays computation until it is actually required, allowing the framework to better estimate the memory requirements of a model before execution. By building a computational graph first and executing operations only when necessary, koila reduces the risk of running out of GPU memory during the forward pass of neural network training. ... -
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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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CTranslate2
Fast inference engine for Transformer models
...The project implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU. The execution is significantly faster and requires less resources than general-purpose deep learning frameworks on supported models and tasks thanks to many advanced optimizations: layer fusion, padding removal, batch reordering, in-place operations, caching mechanism, etc. The model serialization and computation support weights with reduced precision: 16-bit floating points (FP16), 16-bit integers (INT16), and 8-bit integers (INT8). ... -
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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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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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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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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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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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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. ...
