Search Results for "data collection algorithm" - Page 2
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Showing 334 open source projects for "data collection algorithm"
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DreamerV3
Mastering Diverse Domains through World Models
...This approach enables the algorithm to efficiently learn policies for decision-making tasks that would otherwise require enormous amounts of data or computational resources. DreamerV3 was designed as a general reinforcement learning framework that can solve diverse tasks using the same configuration of hyperparameters across many environments. In research demonstrations, the algorithm has been shown to perform strongly across more than one hundred control tasks and complex simulated environments. -
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douyin
Open source Douyin crawler for collecting and downloading public data
DouyinCrawler is an open source data collection tool designed to gather publicly available information from the Douyin platform. It demonstrates how to build a Python-based web crawler combined with a graphical interface and command line functionality. It allows users to collect data from various types of Douyin content, including user profiles, videos, hashtags, and music pages. -
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Flowsint
Graph-based OSINT investigation platform w visual relationship mapping
Flowsint is an open source OSINT investigation platform designed to help analysts explore and understand relationships between digital entities through a visual graph interface. The platform focuses on reconnaissance and open source intelligence workflows, enabling investigators to map connections between domains, IP addresses, organizations, individuals, and other data points. By presenting these relationships in an interactive graph, Flowsint allows users to quickly identify patterns,... -
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crawler
Collection of JS reverse engineering examples for web scraping study
crawler is a collection of web scraping and JavaScript reverse engineering examples designed for learning how modern websites protect their data and how those protections can be analyzed. It contains many case studies that demonstrate how to analyze and replicate request parameters, cookies, and encryption logic used by real websites. Each directory in the project focuses on a specific target service or scenario, showing how browser network requests and JavaScript code can be studied to reproduce API calls programmatically. ... -
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Interpretable machine learning
Book about interpretable machine learning
...Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. An explanation increases the trust in the decision and in the machine learning model. As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the algorithm picked up? This book will give an overview over techniques that can be used to make black boxes as transparent as possible and explain decisions. In the first chapter algorithms that produce simple, interpretable models are introduced together with instructions how to interpret the output. ... -
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Homemade Machine Learning
Python examples of popular machine learning algorithms
homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is pedagogical: you’ll see linear regression, logistic regression, k-means clustering, neural nets, decision trees, etc., built in Python using fundamentals like NumPy and Matplotlib, not hidden behind API calls. ... -
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VikingDB MCP Server
A mcp server for vikingdb store and search
An MCP server that interfaces with VikingDB, a high-performance vector database developed by ByteDance, enabling efficient vector storage and search capabilities. -
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Amazing-Python-Scripts
Curated collection of Amazing Python scripts
...Examples include scripts for sentiment analysis, data scraping, web automation, log analysis, and interactive applications such as games or voice-controlled tools. The project also provides contribution guidelines and documentation so that developers can easily collaborate and expand the collection of scripts. -
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spyder
The scientific Python development environment
Spyder is a free and open source scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts. It features a unique combination of the advanced editing, analysis, debugging, and profiling functionality of a comprehensive development tool with the data exploration, interactive execution, deep inspection, and beautiful visualization capabilities of a scientific package. Spyder’s multi-language Editor integrates a number of powerful tools... -
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GR00T Whole-Body Control (WBC)
Welcome to GR00T Whole-Body Control (WBC)
...It includes the decoupled controller used with GR00T N1.5 and N1.6, combining reinforcement learning for the lower body with inverse kinematics for the upper body. The GEAR-SONIC system learns a broad set of natural motor skills from large-scale human motion data through a unified tracking policy. Released Unitree G1 checkpoints support motion references, whole-body teleoperation, planning, and vision-language-action workflows. A C++ and TensorRT inference stack provides real-hardware deployment, while PyTorch checkpoints support Isaac Lab evaluation and continued training. The repository also includes VR teleoperation, data collection, kinematic planning, and low-latency control configurations. ... -
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Helium Browser
Private, fast, and honest web browser
...Helium blocks ads and trackers by default through an integrated, unbiased uBlock Origin extension prepackaged as a native browser component. Its UI and feature set emphasize minimalism, no “smart” recommendations, account sync, or background data collection, resulting in a distraction-free browsing experience that respects user autonomy. The browser is available across macOS, Linux, and Windows, each version built from a fully open source pipeline for reproducibility and trust. Development focuses on maintaining compatibility with modern web standards while decoupling Chromium from its Google dependencies and services. -
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Professional Services
Common solutions and tools developed by Google Cloud
Professional Services repository is a collection of real-world solutions, tools, and reference implementations developed by Google Cloud’s Professional Services team to address common enterprise challenges. Unlike simple sample repositories, it focuses on production-oriented use cases such as data pipelines, machine learning workflows, infrastructure automation, and security management. -
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FEAPDER
Powerful Python crawler framework for scalable web scraping tasks
feapder is a Python-based web crawling framework designed to simplify the process of building scalable and efficient web scrapers. It focuses on providing a developer-friendly environment that makes it easier to create, run, and manage crawlers for a variety of data collection tasks. It includes several built-in spider types, such as AirSpider, Spider, TaskSpider, and BatchSpider, which address different crawling scenarios ranging from lightweight scraping to distributed and batch-based jobs. feapder supports features such as breakpoint resume, allowing crawlers to continue from where they stopped without losing progress. ... -
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Perceval
An open source framework for programming photonic quantum computers
An open-source framework for programming photonic quantum computers. Through a simple object-oriented Python API, Perceval provides tools for composing circuits from linear optical components, defining single-photon sources, manipulating Fock states, running simulations, reproducing published experimental papers and experimenting with a new generation of quantum algorithms. It aims to be a companion tool for developing photonic circuits – for simulating and optimizing their design, modeling... -
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PaddleX
PaddlePaddle End-to-End Development Toolkit
PaddleX is a deep learning full-process development tool based on the core framework, development kit, and tool components of Paddle. It has three characteristics opening up the whole process, integrating industrial practice, and being easy to use and integrate. Image classification and labeling is the most basic and simplest labeling task. Users only need to put pictures belonging to the same category in the same folder. When the model is trained, we need to divide the training set, the... -
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Bayesian Optimization
Python implementation of global optimization with gaussian processes
This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. More detailed information, other advanced features, and tips on usage/implementation can be found in the examples folder. Follow the basic... -
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openTSNE
Extensible, parallel implementations of t-SNE
openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE) [1], a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings [2], massive speed improvements [3] [4] [5], enabling t-SNE to scale to millions of data points, and various tricks to improve the global alignment of the resulting visualizations. -
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Video-subtitle-remover (VSR)
AI tool that removes hardcoded subtitles and text from videos locally
...In addition to video processing, the project supports removing text-like watermarks from images through similar techniques. The processing runs locally without requiring any external API services, enabling offline use and greater control over the data being processed. -
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Pysheeet
Python Cheat Sheet
Pysheeet is a community-driven collection of Python code snippets covering common patterns and tasks like sockets, file I/O, data structures, and more. Each snippet is concise and battle-tested, designed to save coding time and reduce boilerplate. With documentation hosted on Read the Docs and an active GitHub repo, it’s a go-to resource for Python developers. -
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Deep Lake
Data Lake for Deep Learning. Build, manage, and query datasets
...Use one API to upload, download, and stream datasets to/from AWS S3/S3-compatible storage, GCP, Activeloop cloud, or local storage. Store images, audios and videos in their native compression. Deeplake automatically decompresses them to raw data only when needed, e.g., when training a model. Treat your cloud datasets as if they are a collection of NumPy arrays in your system's memory. Slice them, index them, or iterate through them. -
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Claude Scientific Skills
A set of ready to use Agent Skills for research, science, engineering
Claude Scientific Skills is a large open source collection of ready-to-use scientific capabilities that extend AI coding agents into full research assistants. The project provides more than 170 curated skills covering domains such as genomics, drug discovery, medical imaging, physics, and advanced data analysis. Each skill bundles documentation, examples, and tool integrations so agents can reliably execute complex multi-step scientific workflows. -
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Impacket
A collection of Python classes for working with network protocols
...It features several protocols, including Ethernet, IP, TCP, UDP, ICMP, IGMP, ARP, NMB and SMB1, SMB2 and SMB3 and more. Impacket's object oriented API makes it easy to work with deep hierarchies of protocols. It can construct packets from scratch, as well as parse them from raw data. -
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PyTorch Image Models
The largest collection of PyTorch image encoders / backbones
timm (PyTorch Image Models) is a premier library hosting a vast collection of state-of-the-art image classification models and backbones such as ResNet, EfficientNet, NFNet, Vision Transformer, ConvNeXt, and more. Created by Ross Wightman and now maintained by Hugging Face, it includes pretrained weights, data loaders, augmentations, optimizers, schedulers, and reference scripts for training, evaluation, inference, and model export. -
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Coomer Downloader App
Coomer downloader
...The application typically supports features such as authentication, rate limiting, and retry mechanisms to ensure reliable downloads even when dealing with unstable connections or restricted endpoints. It is often used for personal archiving, data collection, or offline access to content that may otherwise be difficult to manage manually. The tool operates through a command-line interface, making it suitable for scripting and integration into automated workflows. -
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DeepLabCut
Implementation of DeepLabCut
DeepLabCut™ is an efficient method for 2D and 3D markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results (i.e. you can match human labeling accuracy) with minimal training data (typically 50-200 frames). We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. The package is open source, fast, robust, and can be used to compute 3D pose estimates or for multi-animals. Please see the original paper and the latest work below! ...
