Search Results for "network data speed" - Page 5
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Showing 183 open source projects for "network data speed"
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Vibes don’t ship, Retool doesVibe coding tools create cool demos, but Retool helps you build software your company can actually use. Generate internal apps that connect directly to your data—deployed in your cloud with enterprise security from day one. Build dashboards, admin panels, and workflows with granular permissions already in place. Stop prototyping and ship on a platform that actually passes security review.
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Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
...You only look once (YOLO) is a state-of-the-art, real-time object detection system. In the following ROS package, you are able to use YOLO (V3) on GPU and CPU. The pre-trained model of the convolutional neural network is able to detect pre-trained classes including the data set from VOC and COCO, or you can also create a network with your own detection objects. The YOLO packages have been tested under ROS Noetic and Ubuntu 20.04. We also provide branches that work under ROS Melodic, ROS Foxy and ROS2. Darknet on the CPU is fast (approximately 1.5 seconds on an Intel Core i7-6700HQ CPU @ 2.60GHz × 8) but it's like 500 times faster on GPU! ... -
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cnn-for-image-retrieval is a research-oriented project that demonstrates the use of convolutional neural networks (CNNs) for image retrieval tasks. The repository provides implementations of CNN-based methods to extract feature representations from images and use them for similarity-based retrieval. It focuses on applying deep learning techniques to improve upon traditional handcrafted descriptors by learning features directly from data. The code includes training and evaluation scripts that...
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OHHTTPStubs
Stub your network requests easily!
OHHTTPStubs is a library designed to stub your network requests very easily. It can help you test your apps with fake network data (stubbed from file) and simulate slow networks, to check your application behavior in bad network conditions, and write unit tests that use fake network data from your fixtures. OHHTTPStubs headers are fully documented using Appledoc-like / Headerdoc-like comments in the header files. -
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NLP Architect
A model library for exploring state-of-the-art deep learning
...The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab. NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. NLP Architect is a model-oriented library designed to showcase novel and different neural network optimizations. The library contains NLP/NLU-related models per task, different neural network topologies (which are used in models), procedures for simplifying workflows in the library, pre-defined data processors and dataset loaders and misc utilities. ... -
Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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TFLearn
Deep learning library featuring a higher-level API for TensorFlow
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorials and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, and metrics. Full transparency over Tensorflow. ... -
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CyC2018.github.io
Personal knowledge site built with GitHub Pages
This is a personal knowledge site built with GitHub Pages, organizing computer science notes and study materials in a browsable format. It aggregates content across algorithms, data structures, networking, operating systems, databases, and interview preparation into a coherent index. The presentation emphasizes succinct explanations paired with diagrams, tables, or code snippets to speed recall. Because the site is generated from a repository, it benefits from issue tracking, pull requests, and version history, making it easy to update and maintain. ... -
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fastNLP
fastNLP: A Modularized and Extensible NLP Framework
fastNLP is a lightweight framework for natural language processing (NLP), the goal is to quickly implement NLP tasks and build complex models. A unified Tabular data container simplifies the data preprocessing process. Built-in Loader and Pipe for multiple datasets, eliminating the need for preprocessing code. Various convenient NLP tools, such as Embedding loading (including ELMo and BERT), intermediate data cache, etc.. Provide a variety of neural network components and recurrence models (covering tasks such as Chinese word segmentation, named entity recognition, syntactic analysis, text classification, text matching, metaphor resolution, summarization, etc.). ... -
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paroller.js
Parallax scrolling jQuery plugin
paroller.js is a lightweight jQuery plugin that enables parallax scrolling effects on selected elements. To enable the parallax scrolling effect you can use data-patroller-* attributes on selected elements or set values via jQuery. 'factor' sets the speed and distance of the element's parallax effect on scroll. -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than... -
Yeastar: Business Phone System and Unified CommunicationsUser-friendly, optimized, and scalable, the Yeastar P-Series Phone System redefines business connectivity by bringing together calling, meetings, omnichannel messaging, and integrations in one simple platform—removing the limitations of distance, platforms, and systems.
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Euler
A distributed graph deep learning framework.
Data in the fields of text, speech, and images is easier to process into a grid-like type of Euclidean space, which is suitable for processing by existing deep learning models. Graph is a data type in non-Euclidean space and cannot be directly applied to existing methods, requiring a specially designed graph neural network system. Graph-based learning methods such as graph neural networks combine end-to-end learning with inductive reasoning, and are expected to solve a series of problems such as relational reasoning and interpretability that deep learning cannot handle. -
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Brand new cheatsheets and handouts
Matplotlib 3.1 cheat sheet
...It lays out common use cases (plot types, styling, figure configuration, saving/exporting, subplot layout, etc.) in a concise and organized format — often serving as a “cheat sheet” for rapid look-up. For practitioners working on data-heavy projects, dashboards, or research code where plotting is frequent, it helps speed up development by reducing context-switching and documentation navigation overhead. It is especially useful when you know roughly what you want (e.g. “I need a scatter + histogram marginal plot”) but don’t remember the exact Matplotlib call. -
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usbmuxd
A socket daemon to multiplex connections from and to iOS devices
...You should also create an usbmux user that has access to USB devices on your system. Alternatively, just pass a different username using the -U argument. usbmuxd is not used for tethering data transfers which uses a dedicated USB interface to act as a virtual network device. The higher-level layers, especially if you want to write an application to interact with the device, are handled by libimobiledevice. -
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WebRTC Android
webrtc VideoCall VideoConference
WebRTC Android is a sample project that showcases how to implement real-time peer-to-peer communication (audio, video, and data) on Android using WebRTC. It demonstrates a complete pipeline—from signaling and connection setup to media capture and transmission—making it an excellent reference for developers looking to integrate WebRTC into their mobile apps. The project includes UI components and handles network state changes, codec configuration, and ICE negotiation to provide a robust base for real-world applications. -
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The Neural Process Family
This repository contains notebook implementations
Neural Processes (NPs) is a collection of interactive Jupyter/Colab notebook implementations developed by Google DeepMind, showcasing three foundational probabilistic machine learning models: Conditional Neural Processes (CNPs), Neural Processes (NPs), and Attentive Neural Processes (ANPs). These models combine the strengths of neural networks and stochastic processes, allowing for flexible function approximation with uncertainty estimation. They can learn distributions over functions from... -
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OkReplay
Record and replay OkHttp network interaction in your tests
OkReplay is a tool for recording and replaying HTTP interactions (specifically via OkHttp) in Android tests to improve determinism, test speed, and reliability. During the first run of a test annotated with @OkReplay, the library records outgoing HTTP requests and their responses into tape files. On subsequent runs, it intercepts those requests and serves the recorded responses instead of making real network calls. This allows tests to be executed offline, reduces flakiness due to network variability, and isolates the test environment from external dependencies. ... -
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Graph Nets library
Build Graph Nets in Tensorflow
Graph Nets, developed by Google DeepMind, is a Python library designed for constructing and training graph neural networks (GNNs) using TensorFlow and Sonnet. It provides a high-level, flexible framework for building neural architectures that operate directly on graph-structured data. A graph network takes graphs as inputs, consisting of edges, nodes, and global attributes, and produces updated graphs with modified feature representations at each level. This library implements the foundational ideas from DeepMind’s paper “Relational Inductive Biases, Deep Learning, and Graph Networks”, offering tools to explore relational reasoning and message-passing neural networks. ... -
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EasyNetVars
Data Exchange between CoDeSys Devices and .NET via Network Variables
Library (.DLL) for Data Exchange between CoDeSys Devices and .NET and Step 7 via Network-Variables written in C# V1.0: Bugfixes New 02/2015: Read and Write Operations possible for more than 255 byte. - Read and Write Operations CoDeSys -> .NET - Method to create .GVL-File to import in CoDeSys Example in Step 7 (TIA-Portal) for Data-exchange between CoDeSys and Siemens S7 Devices CoDeSys -> Step7 (S7-1200): https://sourceforge.net/projects/easynetvars/files/NetVarsStep7/ See WIKI and Implementation-Guide for more information -
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VivaGraph
Graph drawing library for JavaScript
...VivaGraphJS is modular, so you can extend or customize layouts, rendering, and interaction logic to fit specialized applications such as social network analysis, dependency mapping, or knowledge graphs. -
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I3D models trained on Kinetics
Convolutional neural network model for video classification
Kinetics-I3D, developed by Google DeepMind, provides trained models and implementation code for the Inflated 3D ConvNet (I3D) architecture introduced in the paper “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset” (CVPR 2017). The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with... -
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NetworkEye
A iOS network debug library, monitor HTTP requests
NetworkEye, an iOS network debug library, monitors HTTP requests. It can be detected HTTP requests including web pages, NSURLConnection, NSURLSession, AFNetworking, 3rd libraries, 3rd SDK, and so on. very convenient and practical. It can be a map local json file. -
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benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. ... -
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TenorSpace.js
Neural network 3D visualization framework
...After preprocessing the model, TensorSpace supports the visualization of pre-trained models from TensorFlow, Keras and TensorFlow.js. TensorSpace is a neural network 3D visualization framework designed for not only showing the basic model structure but also presenting the processes of internal feature abstractions, intermediate data manipulations and final inference generations. By applying TensorSpace API, it is more intuitive to visualize and understand any pre-trained models built by TensorFlow, Keras, TensorFlow.js, etc. -
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concordia
Powerful search library, best suited for computer-aided translation
...It uses a RAM stored index, which takes up approximately 600MB of memory for a corpus of 2 million sentences. It is based on the idea of a suffix array, enhanced by the presence of other auxiliary data structures. The effects are stunning - Concordia is able to do simple substring lookup at the pace of 5000 queries per second (on personal PC) - a speed which can not be achieved by any other search library. Moreover, Concordia can perform its own "concordia search". For a given input sentece, all substring matches covering this sentence are retrieved. ... -
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SFD
S³FD: Single Shot Scale-invariant Face Detector, ICCV, 2017
...It includes training scripts, evaluation code, and pre-trained models that achieve strong results on popular benchmarks such as AFW, PASCAL Face, FDDB, and WIDER FACE. The framework is optimized for speed and accuracy, making it suitable for both academic research and practical applications in computer vision. -
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sjcl
Stanford Javascript Crypto Library
The Stanford Javascript Crypto Library is a project by the Stanford Computer Security Lab to build a secure, powerful, fast, small, easy-to-use, cross-browser library for cryptography in Javascript. SJCL is small but powerful. The minified version of the library is under 6.4KB compressed, and yet it posts impressive speed results. SJCL is secure. It uses the industry-standard AES algorithm at 128, 192 or 256 bits; the SHA256 hash function; the HMAC authentication code; the PBKDF2 password...
