Search Results for "deep" - Page 7
Sort By:
Showing 268 open source projects for "deep"
View related business solutions-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
-
1
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... -
2
hapi
The simple, secure framework developers trust
...Originally developed to handle Walmart’s Black Friday scale, hapi continues to be the proven choice for enterprise-grade backend needs. When you npm install @hapi/hapi, every single line of code you get has been verified. You never have to worry about some deep dependency being poorly maintained (or handed over to someone sketchy). hapi is the only leading node framework without any external code dependencies. None. hapi has been pushing the envelope on quality from day one. It was the first node framework to require and achieve 100% code coverage across every dependency – when everyone thought it was crazy. ... -
3
deep-clean
When Gradle or the IDE let you down, just --nuke all them caches
A Kotlin script that nukes all build caches from Gradle/Android projects. Useful when Gradle or the IDE let you down. The script has been tested on macOS, but it is completely untested on Linux and Windows. KScript may not work at all on Windows! For this script to work, you need to have kotlin, script, and maven on your PATH. If you don't have all three commands on your PATH, then read on to the next section to install them. To make the script run, we'll first need to install all the... -
4
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. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
5
graphql-api-koa
GraphQL execution and error handling middleware written from scratch
GraphQL execution and error handling middleware written from scratch for Koa. -
6
Real-ESRGAN
Real-ESRGAN aims at developing Practical Algorithms
Real-ESRGAN is a highly popular open-source project that provides practical algorithms for general image and video restoration using deep learning-based super-resolution techniques. It extends the original Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) approach by training on synthetic degradations to make results more robust on real-world images, effectively enhancing resolution, reducing noise/artifacts, and reconstructing fine detail in low-quality imagery. ... -
7
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. ... -
8
Reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI
...It’s structured to serve learners progressing from basic tabular methods to function approximation and deep learning extensions, making it suitable for students, researchers, or practitioners exploring reinforcement learning fundamentals. -
9
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. -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
10
Spring Boot Demo
A project for in-depth study and practical application of Spring Boot
This repository is a hands-on, “deep learning by doing” collection of Spring Boot demos that you can run and study module by module. It currently includes 66 planned integrations (with 55 completed) spanning monitoring, logging, templating, data access, caching, messaging, scheduling, search, security, and more. The master branch targets Spring Boot 2.1.0.RELEASE with a parent POM that centralizes common dependency versions; the older v-1.5.x branch is frozen and its contents are being migrated to master. ... -
11
Codeball AI
AI Code Review that finds bugs and fast-tracks your code
...Fully customizable and programmable with GitHub Actions. Codeball Actions are built on multiple smaller building blocks, that are heavily configurable through GitHub Actions. Codeball uses a deep learning model that has been trained on over 1 million Pull Requests. For each contribution, it considers hundreds of inputs. Codeball is optimized for precision, which means it only approves contributions that it's really confident in. -
12
baloo
Expressive end-to-end HTTP API testing made easy in Go
Expressive and versatile end-to-end HTTP API testing made easy in Go (golang), built on top of the gentleman HTTP client toolkit. -
13
SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
...This library provides default pre-processing, predict and postprocessing for certain MXNet model types and utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). ... -
14
Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable. -
15
pyTorch Tutorials
Build your neural network easy and fast
pyTorch Tutorials is an open-source collection of hands-on tutorials designed to teach developers how to build neural networks with the PyTorch framework. It covers the fundamentals of PyTorch from basic tensor operations to constructing full neural network models, making it suitable for beginners and intermediate learners alike. The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks,... -
16
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. -
17
Big Sleep
A simple command line tool for text to image generation
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU. You will be able to have the GAN dream-up images using natural language with a one-line command in the terminal. User-made notebook with bug fixes and added features, like google drive integration. Images will be saved to... -
18
Test Manager plugin for Trac
A Trac plugin manage the quality process
A Trac plugin to create Test Cases, organize them in catalogs and track their execution status and outcome. Take a look at the Web Page on trac-hacks: http://trac-hacks.org/wiki/TestManagerForTracPlugin -
19
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. ... -
20
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... -
21
Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll open source more research projects in this way. It trains much faster. Models can be exported to TorchScript format or Caffe2 format for deployment. ... -
22
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... -
23
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. -
24
PyTorchVideo
A deep learning library for video understanding research
PyTorchVideo is a deep learning library for video understanding, providing modular components and pretrained models for tasks like action recognition, video classification, detection, and self-supervised learning. It is tightly integrated with PyTorch and PyTorch Lightning, offering flexible APIs for building and training spatiotemporal networks. The library includes efficient implementations of state-of-the-art architectures such as SlowFast, X3D, and MViT, optimized for both research prototyping and production inference. ... -
25
ReconSpider
Most Advanced Open Source Intelligence (OSINT) Framework
ReconSpider is most Advanced Open Source Intelligence (OSINT) Framework for scanning IP Addresses, Emails, Websites, and Organizations and find out information from different sources. ReconSpider can be used by Infosec Researchers, Penetration Testers, Bug Hunters, and Cyber Crime Investigators to find deep information about their target. ReconSpider aggregate all the raw data, visualize it on a dashboard, and facilitate alerting and monitoring on the data. Recon Spider also combines the capabilities of Wave, Photon and Recon Dog to do a comprehensive enumeration of attack surfaces. Reconnaissance is a mission to obtain information by various detection methods, about the activities and resources of an enemy or potential enemy, or geographic characteristics of a particular area. ...
