Search Results for "deep" - Page 3
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Showing 131 open source projects for "deep"
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zshy
Bundler-free build tool for TypeScript libraries
...Instead of relying on bundlers like Webpack or tsup, zshy leverages the TypeScript compiler (tsc) itself to produce both ESM and CommonJS builds, generate type declarations, and automatically populate "exports" fields in a package’s package.json. It reads configuration directly from package.json and standard TypeScript config files, doesn’t require its own config, and supports multi-entry libraries with deep wildcard exports. Originally created for building popular libraries such as Zod, zshy fills the niche for a straightforward, convention-over-configuration build pipeline for TypeScript packages. The tool copies non-JS assets, supports .tsx, and makes CLI packaging easy, helping maintain consistent library outputs with minimal boilerplate. -
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Anomalib
An anomaly detection library comprising state-of-the-art algorithms
Anomalib is an open-source deep learning library focused on anomaly detection and localization tasks, collecting state-of-the-art algorithms and tools under one modular framework. It provides implementations of leading anomaly detection methods drawn from current research, as well as a full set of utilities for training, evaluating, benchmarking, and deploying these models on both public and private datasets. -
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Infosec Reference
An Information Security Reference That Doesn't Suck
Infosec Reference is a curated knowledge base and resource repository for information security practitioners. It aggregates cheat sheets, tooling guides, protocol deep dives, incident response playbooks, and threat actor profiles—all organized under accessible categories (network, web, host, cryptography, auditing). The repo is built as a living wiki of sorts: practitioners contribute updates, expand sections, or refine explanations as the threat landscape evolves. Because security spans many domains, Infosec Reference helps consolidate high-value, battle-tested knowledge into one place, reducing the need to scour scattered blogs or notes. ... -
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type-fest
A collection of essential TypeScript types
type-fest is a TypeScript utility types library that offers a curated, battle-tested suite of type definitions and type transformations that aren’t included in the TypeScript standard library. It provides types like Except, Merge, LiteralUnion, Writable, Promisable, PartialDeep, JsonObject, and many others that solve everyday typing needs in complex TypeScript codebases. Developers pull in just the types they need, which makes code more expressive and safer without reinventing tricky type... -
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Scalaz
Principled Functional Programming in Scala
...Scalaz also contributes optics, equality/ordering abstractions, and lawful instances with property-based tests to ensure algebraic laws hold. While the Scala ecosystem now includes sibling projects, Scalaz remains a deep toolbox for principled FP in Scala and a reference point for category-theory-inspired design on the JVM. -
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MASA Blazor
Blazor UI component library based on Material Design
Provide a standard basic component library based on Material design specifications. Provides preset components for standard scenarios such as layout, frame standard, Loading, and global exception handling. Starting from more practical scenarios, to meet the needs of more users and scenarios, and to minimize the time cost of developers. Shorten the development cycle and improve development efficiency. And provide a set of examples of Web solutions - MASA Blazor Pro has a variety of common... -
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DeepXDE
A library for scientific machine learning & physics-informed learning
DeepXDE is a library for scientific machine learning and physics-informed learning. DeepXDE includes the following algorithms. Physics-informed neural network (PINN). Solving different problems. Solving forward/inverse ordinary/partial differential equations (ODEs/PDEs) [SIAM Rev.] Solving forward/inverse integro-differential equations (IDEs) [SIAM Rev.] fPINN: solving forward/inverse fractional PDEs (fPDEs) [SIAM J. Sci. Comput.] NN-arbitrary polynomial chaos (NN-aPC): solving... -
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Kornia
Open Source Differentiable Computer Vision Library
...Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors. With Kornia we fill the gap between classical and deep computer vision that implements standard and advanced vision algorithms for AI. Our libraries and initiatives are always according to the community needs. -
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AudioCraft
Audiocraft is a library for audio processing and generation
AudioCraft is a PyTorch library for text-to-audio and text-to-music generation, packaging research models and tooling for training and inference. It includes MusicGen for music generation conditioned on text (and optionally melody) and AudioGen for text-conditioned sound effects and environmental audio. Both models operate over discrete audio tokens produced by a neural codec (EnCodec), which acts like a tokenizer for waveforms and enables efficient sequence modeling. The repo provides... -
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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paraffin
Iterative and recursive parallelism generics for Ada 2005 and Ada 2012
A suite of Ada 2012 generics to facilitate iterative and recursive parallelism for multicore systems and provide safer recursion for single and multicore systems. Generics include Ravenscar compatible versions for real-time systems. Also Includes paraffinalia, which is a set of useful generics for parallel quicksort, fast fourier transform, function integration, prefix sum, and Red-Black trees -
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MMDeploy
OpenMMLab Model Deployment Framework
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. -
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fastMRI
A large open dataset + tools to speed up MRI scans using ML
fastMRI is a large-scale collaborative research project by Facebook AI Research (FAIR) and NYU Langone Health that explores how deep learning can accelerate magnetic resonance imaging (MRI) acquisition without compromising image quality. By enabling reconstruction of high-fidelity MR images from significantly fewer measurements, fastMRI aims to make MRI scanning faster, cheaper, and more accessible in clinical settings. The repository provides an open-source PyTorch framework with data loaders, subsampling utilities, reconstruction models, and evaluation metrics, supporting both research reproducibility and practical experimentation. ... -
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DeepMind Research
Implementations and code to accompany DeepMind publications
This repository collects reference implementations and illustrative code accompanying a wide range of DeepMind publications, making it easier for the research community to reproduce results, inspect algorithms, and build on prior work. The top level organizes many paper-specific directories across domains such as deep reinforcement learning, self-supervised vision, generative modeling, scientific ML, and program synthesis—for example BYOL, Perceiver/Perceiver IO, Enformer for genomics, MeshGraphNets for physics, RL Unplugged, Nowcasting for weather, and more. Each project folder typically includes its own README, scripts, and notebooks so you can run experiments or explore models in isolation, and many link to associated datasets or external environments like DeepMind Lab and StarCraft II. ... -
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DIG
A library for graph deep learning research
The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. -
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Index Advisor
PostgreSQL Index Advisor
index_advisor is a PostgreSQL extension developed by Supabase that suggests index optimizations based on query performance. It passively monitors query patterns and recommends indexes to improve performance without requiring deep database expertise. This tool helps developers and DBAs reduce query latency and improve efficiency in production environments. -
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Scalable-Software-Architecture
Collection of tech talks, papers and web links on Distributed Systems
Scalable-Software-Architecture gathers tech talks, academic papers, blog posts and web links related to building scalable software architecture, distributed systems, system design, and production-scale considerations. The aim is to help readers study how large systems operate: how to scale them, how to handle latency, failure, load, concurrency, and how to structure software for maintainability at scale. Because many interview and real-world engineering problems revolve around system design... -
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Neural Network Visualization
Project for processing neural networks and rendering to gain insights
nn_vis is a minimalist visualization tool for neural networks written in Python using OpenGL and Pygame. It provides an interactive, graphical representation of how data flows through neural network layers, offering a unique educational experience for those new to deep learning or looking to explain it visually. By animating input, weights, activations, and outputs, the tool demystifies neural network operations and helps users intuitively grasp complex concepts. Its lightweight codebase is great for customization and teaching purposes. -
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ml-surveys
Survey papers summarizing advances in deep learning, NLP, CV, graphs
The ml-surveys repository is a broad, maintainable overview of survey papers across many subfields of machine learning — including deep learning, NLP, computer vision, graph ML, reinforcement learning, recommendation systems, embeddings, meta-learning, and more. Instead of diving into code or experiments, this repo gathers authoritative survey and review articles, summarizing the state-of-the-art, trends, challenges, and directions within each subdomain. For someone trying to get up to speed with a new ML subfield — say graph neural networks or meta-learning — ml-surveys offers a curated reading list of foundational and recent works, helping map the landscape quickly. ... -
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CommandlineConfig
A library for users to write configurations in Python
...One of its core strengths is the ability to override configuration values directly from the command line, making it convenient to run many experimental variants without editing files repeatedly. The library supports arbitrarily deep nested structures, type handling, enumerated value constraints, and even tuple types, which are common in ML experiment setups. It also includes features for automatic version checking and convenient help output, so users can quickly see available parameters and their descriptions via a -h flag. -
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Flashlight library
A C++ standalone library for machine learning
Flashlight is a fast, flexible machine learning library written entirely in C++ by Facebook AI Research and the creators of Torch, TensorFlow, Eigen, and Deep Speech. Native support in C++ and simple extensibility make Flashlight a powerful research framework that's hackable to its core and enables fast iteration on new experimental setups and algorithms with little unopinionated and without sacrificing performance. In a single repository, Flashlight provides apps for research across multiple domains. ... -
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Graph4NLP
Graph4nlp is the library for the easy use of Graph Neural Networks
Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing (i.e., DLG4NLP). It provides both full implementations of state-of-the-art models for data scientists and also flexible interfaces to build customized models for researchers and developers with whole-pipeline support. Built upon highly-optimized runtime libraries including DGL , Graph4NLP has both high running efficiency and great extensibility. -
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SVoice (Speech Voice Separation)
We provide a PyTorch implementation of the paper Voice Separation
SVoice is a PyTorch-based implementation of Facebook Research’s study on speaker voice separation as described in the paper “Voice Separation with an Unknown Number of Multiple Speakers.” This project presents a deep learning framework capable of separating mixed audio sequences where several people speak simultaneously, without prior knowledge of how many speakers are present. The model employs gated neural networks with recurrent processing blocks that disentangle voices over multiple computational steps, while maintaining speaker consistency across output channels. ... -
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deeplinks.js
Simple deep links to any selection of text on your website
deeplinks.js allows people to easily link directly to any text selection on your website. Here's an example of what one of these links looks like. It's intended mostly for blogs and other such websites, but it's relatively agnostic to the environment it's running in. People can select text on the site as the normally would, and when they do, the fragment identifier (the thing that comes after the # in the URL) changes. If they want to share the text they have selected, they simply copy the... -
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TensorFlow Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
TensorFlow Examples is a comprehensive repository of example implementations, tutorials, and reference code intended to help newcomers and intermediate learners dive into TensorFlow quickly. It contains both Jupyter notebooks and raw source code, covering a broad range of tasks: from basic machine-learning and neural-network models to more advanced use cases, using both TensorFlow v1 and v2 APIs. For clarity and educational value, each example is accompanied by explanatory comments or... -
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libpython-clj
Python bindings for Clojure
libpython-clj is a deep interop library enabling you to load and use Python modules from within Clojure as if they were native namespaces—and even extend Python objects from Clojure. It bridges to the Python C API, preserving REPL‑based workflows. Bridge between JVM objects and Python objects easily; use Python in your Java and use some Java in your Python.
