Search Results for "data capture framework" - Page 15
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Showing 551 open source projects for "data capture framework"
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AllenNLP
An open-source NLP research library, built on PyTorch
AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment). AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that provides custom registered classes or additional... -
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Emb-GAM
An interpretable and efficient predictor using pre-trained models
Deep learning models have achieved impressive prediction performance but often sacrifice interpretability, a critical consideration in high-stakes domains such as healthcare or policymaking. In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. In this work, we aim to bridge this gap by using pre-trained neural language models to extract embeddings for... -
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Pattern
Web mining module for Python, with tools for scraping
Pattern is an open-source Python library that provides tools for web mining, natural language processing, machine learning, and network analysis. The project integrates multiple capabilities into a single framework that allows developers to collect, process, and analyze textual data from the web. It includes modules for web scraping and crawling that can retrieve information from sources such as social media platforms, search engines, and online knowledge bases. In addition to data mining features, the library offers natural language processing functionality including part-of-speech tagging, sentiment analysis, and n-gram extraction. ... -
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Jraph
A Graph Neural Network Library in Jax
Jraph (pronounced “giraffe”) is a lightweight JAX library developed by Google DeepMind for building and experimenting with graph neural networks (GNNs). It provides an efficient and flexible framework for representing, manipulating, and training models on graph-structured data. The core of Jraph is the GraphsTuple data structure, which enables users to define graphs with arbitrary node, edge, and global attributes, and to batch variable-sized graphs efficiently for JAX’s just-in-time compilation. The library includes a comprehensive set of utilities for batching, padding, masking, and partitioning graph data, making it ideal for distributed and large-scale GNN experiments. ... -
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Python 3 Network Packet Sniffer
A Network Packet Sniffing tool developed in Python 3
A Network Packet Sniffer developed in Python 3. Packets are disassembled as they arrive at a given network interface controller and their information is displayed on the screen. This application depends exclusively on the NETProtocols library (also developed and maintained by EONRaider) from version 2.0.0 and above and can be run by any Python 3.8+ interpreter. -
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Blank Grabber
The most powerful stealer written in Python 3
Blank Grabber is an archived Python information-stealing malware project presented as a configurable grabber with a graphical builder. The repository lists capabilities related to credential theft, session theft, browser data collection, system information capture, screenshots, webcam capture, persistence, evasion, and exfiltration. Although the README includes an educational disclaimer, the tool’s stated behavior is clearly harmful outside isolated and authorized security research. It should not be used as a normal utility, administration tool, or learning project for general users. ... -
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dispy
Distributed and Parallel Computing with/for Python.
dispy is a generic and comprehensive, yet easy to use framework for creating and using compute clusters to execute computations in parallel across multiple processors in a single machine (SMP), among many machines in a cluster, grid or cloud. dispy is well suited for data parallel (SIMD) paradigm where a computation (Python function or standalone program) is evaluated with different (large) datasets independently. -
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pspider
Simple Python framework for building multithreaded web crawlers
PSpider is a lightweight web crawling framework written in Python designed to simplify the development of custom web spiders. It focuses on providing an easy-to-understand architecture while still supporting concurrent crawling for improved performance. It uses a multithreaded model that separates the crawling workflow into several components responsible for fetching, parsing, and saving data. -
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Blankly
Easily build, backtest and deploy your algo in just a few lines
Blankly is a live trading engine, backtest runner and development framework wrapped into one powerful open-source package. Models can be instantly backtested, paper traded, sandbox tested and run live by simply changing a single line. We built blankly for every type of quant including training & running ML models in the same environment, cross-exchange/cross-symbol arbitrage, and even long/short positions on stocks (all with built-in WebSockets). Blankly is the first framework to enable... -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make... -
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TensorFlowOnSpark
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters
By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. It enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the amount of code changes required to run existing TensorFlow programs on a shared grid. -
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MAE (Masked Autoencoders)
PyTorch implementation of MAE
MAE (Masked Autoencoders) is a self-supervised learning framework for visual representation learning using masked image modeling. It trains a Vision Transformer (ViT) by randomly masking a high percentage of image patches (typically 75%) and reconstructing the missing content from the remaining visible patches. This forces the model to learn semantic structure and global context without supervision. The encoder processes only the visible patches, while a lightweight decoder reconstructs the... -
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MeshCNN in PyTorch
Convolutional Neural Network for 3D meshes in PyTorch
MeshCNN is a deep learning framework designed specifically for processing 3D triangular mesh data using convolutional neural networks. Unlike traditional CNNs that operate on images or voxel grids, MeshCNN performs convolution operations directly on the edges of mesh structures. This design allows the model to capture geometric relationships between mesh elements while preserving the underlying topology of 3D shapes. -
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SysBioTK
A protein database management toolkit
SysBioTK is a framework which aims to aid in the management of information on proteins from several databases, storing the information in a library. Several search functions are also included in order to filter the proteins in the library. Support for different screenings, such as control groups, is also included. The tool also allows to extract the GeneOntology of the protein/gene list and is able to perform several statiscal tests on the data. -
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Alfi Face
Face Recognition based Attendance System for school, college...
ALFI FACE uses facial recognition technology to record the attendance through a digital camera that detects and recognizes faces and compare the faces with students’ faces images stored in faces database. Once the recognized face matches a stored image, attendance is marked in attendance database for that person. Note: While adding a new student you have to click on" Train the Recognizer" button . In excel sheet dates are "#" so that no one can change the dates. -
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Admin4
portable administration tool for LDAP, DNS, IMAP, PostgreSQL and more
...Custom objectClasses and attributes can easily be added. The IMAP plugin can browse and modify mailbox metadata, such as name, acl and quota. The PostgreSQL plugin features a nice query tool, a data modification tool and an object browser. The development has moved to https://github.com/andreas-p/admin4, including all release files. -
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VSGAN
VapourSynth Single Image Super-Resolution Generative Adversarial
Single Image Super-Resolution Generative Adversarial Network (GAN) which uses the VapourSynth processing framework to handle input and output image data. Transform, Filter, or Enhance your input video, or the VSGAN result with VapourSynth, a Script-based NLE. You can chain models or re-run the model twice-over (or more). Have low VRAM? Don’t worry! The Network will be applied in quadrants of the image to reduce up-front VRAM usage. You can use any RGB video input, including float32 (e.g., RGBS) inputs. ... -
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ARPT-Bot
Tool designed to interact with streaming or media-related platforms
ARPT-Bot is an automation tool designed to interact with streaming or media-related platforms, providing automated recording, monitoring, or processing capabilities. It is built to handle repetitive tasks such as tracking live streams, managing content, or executing scripted workflows. The system typically integrates with multimedia tools like FFmpeg to process or capture streams efficiently. It emphasizes automation and reliability, enabling long-running tasks without manual intervention.... -
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pyWhat
Identify emails, IP addresses, and more
pyWhat is a Python-based identification tool designed to figure out “what” a piece of text or file content represents, especially in security and OSINT workflows. Given inputs such as hex strings, URLs, email addresses, IP addresses, credit card numbers, cryptocurrency wallets, or entire .pcap capture files, it scans for structured patterns and tells you what it finds. The tool is recursive: it can traverse files and directories to extract meaningful entities, which is useful when analyzing... -
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StreamAlert
StreamAlert is a serverless, realtime data analysis framework
StreamAlert is a serverless, real-time data analysis framework that empowers you to ingest, analyze, and alert on data from any environment, using data sources and alerting logic you define. Computer security teams use StreamAlert to scan terabytes of log data every day for incident detection and response. Incoming log data will be classified and processed by the rules engine. -
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SparrowRecSys
A Deep Learning Recommender System
SparrowRecSys is an open-source deep learning recommendation system framework designed to demonstrate the architecture and implementation of modern industrial-scale recommender systems. The project integrates multiple machine learning models and data processing pipelines to simulate how real-world recommendation platforms operate. It includes components for offline data processing, feature engineering, model training, real-time data updates, and online recommendation services. ... -
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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. -
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PyTorchVideo
A deep learning library for video understanding research
...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. It supports video I/O pipelines, data augmentation, distributed training, and mixed precision computation for large-scale experiments. PyTorchVideo also connects seamlessly with other Meta AI tools such as Detectron2 and PyTorch3D for multimodal video analysis. Designed to accelerate research and deployment, it serves as a unified framework for reproducible, high-performance video AI development. -
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restless
A lightweight REST miniframework for Python
A lightweight REST mini framework for Python. Works great with Django, Flask, Pyramid & Tornado, but should be useful for many other Python web frameworks. Based on the lessons learned from Tastypie & other REST libraries. Restless tries to be RESTful by default, but flexible enough. The main Resource class has data methods (that you implement) for all the main RESTful actions. -
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Glazier
A tool for automating the installation of Windows OS
Glazier is an automation framework developed by Google for deploying and managing Windows operating systems at scale. It streamlines the entire Windows imaging process by booting systems into the Windows Preinstallation Environment (WinPE), retrieving installation instructions from a web server, and automatically applying operating systems, software, and configurations.
