Search Results for "data collection algorithm" - Page 3
Sort By:
Showing 134 open source projects for "data collection algorithm"
View related business solutions-
Simple, Secure Domain RegistrationRegister or renew your domain and pay only what we pay. No markups, hidden fees, or surprise add-ons. Choose from over 400 TLDs (.com, .ai, .dev). Every domain is integrated with Cloudflare's industry-leading DNS, CDN, and free SSL to make your site faster and more secure. Simple, secure, at-cost domain registration.
-
Gen AI apps are built with MongoDB AtlasMongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
-
1
stkpp
C++ Statistical ToolKit
...The library offers a dense set of (mostly) template classes in C++ and is suitable for projects ranging from small one-off projects to complete data mining application suites. -
2
ChatGPT Plugins Collection
An unofficial collection of Plugins for ChatGPT
ChatGPT-Plugins-Collection is a community-driven repository that gathers examples and resources for building, testing, and experimenting with ChatGPT plugins. The collection provides a variety of plugin implementations that showcase different use cases, helping developers learn how to extend ChatGPT’s functionality. It is designed to serve both as a learning resource for beginners and a reference point for more experienced developers. -
3
TXM
Unicode XML TEI text analysis platform
TXM is a free and open-source cross-platform Unicode & XML based text analysis environment and graphical client, supporting Windows, Linux and Mac OS X. It can also be used online as a J2EE standard compliant web portal (GWT based) with access control built in. DOWNLOAD LATEST VERSION OF TXM : http://textometrie.ens-lyon.fr/spip.php?rubrique61&lang=en TXM offers a comprehensive range of analysis tools (concordances, collocate search, frequency lists, etc.) based on the powerfull CQP... -
4
LRSLibrary
Low-Rank and Sparse Tools for Background Modeling and Subtraction
...The algorithms can also be adapted to other computer vision or machine learning problems beyond video. Large algorithm collection: > 100 matrix- and tensor-based low-rank + sparse methods. Open-source license, documentation and references included. -
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.
-
5
OGB
Benchmark datasets, data loaders, and evaluators for graph machine
The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner. OGB is a community-driven initiative in active development. We expect the benchmark datasets to evolve. -
6
FFCV
Fast Forward Computer Vision (and other ML workloads!)
ffcv is a drop-in data loading system that dramatically increases data throughput in model training. From gridding to benchmarking to fast research iteration, there are many reasons to want faster model training. Below we present premade codebases for training on ImageNet and CIFAR, including both (a) extensible codebases and (b) numerous premade training configurations. -
7
GFPGAN
GFPGAN aims at developing Practical Algorithms
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration. Colab Demo for GFPGAN; (Another Colab Demo for the original paper model) Online demo: Huggingface (return only the cropped face) Online demo: Replicate.ai (may need to sign in, return the whole image). Online demo: Baseten.co (backed by GPU, returns the whole image). We provide a clean version of GFPGAN, which can run without CUDA extensions. So that it can run in Windows or on CPU mode. GFPGAN aims at developing... -
8
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... -
9
Gym
Toolkit for developing and comparing reinforcement learning algorithms
...Open source interface to reinforce learning tasks. The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. The gym library is a collection of test problems — environments — that you can use to work out your reinforcement learning algorithms. ... -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
-
10
Auto-PyTorch
Automatic architecture search and hyperparameter optimization
While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, another trend in AutoML is to focus on neural architecture search. To bring the best of these two worlds together, we developed Auto-PyTorch, which jointly and robustly optimizes the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch is mainly developed to support tabular data (classification, regression) and time series... -
11
Weka
Machine learning software to solve data mining problems
Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. The algorithms can either be applied directly to a dataset or called from your own Java code. -
12
Awesome Explainable Graph Reasoning
A collection of research papers and software related to explainability
A collection of research papers and software related to explainability in graph machine learning. Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability. This limitation can be circumvented by developing post hoc techniques to explain the predictions, giving rise to the area of explainability. -
13
Photonix Photo Manager
A modern, web-based photo management server
...I'd love for other contributors to get involved. You can move some photos into the folder data/photos and they should get detected and imported immediately. Once you have finished trying out the system you can edit the volume in the docker-compose.yml file where it says ./data/photos to mount wherever you usually keep photos. System database, thumbnails and other cache data is stored separately from the photos so shouldn't pollute the area. -
14
ReinventCommunity
Jupyter Notebook tutorials for REINVENT 3.2
This repository is a collection of useful jupyter notebooks, code snippets and example JSON files illustrating the use of Reinvent 3.2. -
15
Objectron
A dataset of short, object-centric video clips
The Objectron dataset is a collection of short, object-centric video clips, which are accompanied by AR session metadata that includes camera poses, sparse point-clouds and characterization of the planar surfaces in the surrounding environment. In each video, the camera moves around the object, capturing it from different angles. The data also contain manually annotated 3D bounding boxes for each object, which describe the object’s position, orientation, and dimensions. ... -
16
BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events... -
17
CC-Net
Tools to download and cleanup Common Crawl data
cc_net provides tools to download, segment, clean, and filter Common Crawl to build large-scale text corpora, including monolingual datasets and the multilingual CC-100 collection introduced in the associated paper. It includes pipelines to fetch snapshots, extract text, de-duplicate, identify language, and apply quality filtering based on heuristics and language models. The outputs are intended for pretraining language models and for creating standardized corpora that can be reproduced or... -
18
CNN Explainer
Learning Convolutional Neural Networks with Interactive Visualization
In machine learning, a classifier assigns a class label to a data point. For example, an image classifier produces a class label (e.g, bird, plane) for what objects exist within an image. A convolutional neural network, or CNN for short, is a type of classifier, which excels at solving this problem! A CNN is a neural network: an algorithm used to recognize patterns in data. Neural Networks in general are composed of a collection of neurons that are organized in layers, each with their own learnable weights and biases. ... -
19
Computer Vision Pretrained Models
A collection of computer vision pre-trained models
...A pre-trained model may not be 100% accurate in your application. For example, if you want to build a self-learning car. You can spend years building a decent image recognition algorithm from scratch or you can take the inception model (a pre-trained model) from Google which was built on ImageNet data to identify images in those pictures. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone. TensorFlow implementation of 'YOLO: Real-Time Object Detection', with training and an actual support for real-time running on mobile devices. ... -
20
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... -
21
Machine Learning Homework
Matlab Coding homework for Machine Learning
The Machine-Learning-homework repository by user “Ayatans” is a collection of MATLAB code intended to solve or illustrate assignments in machine learning courses. It includes implementations of standard machine learning algorithms (such as regression, classification, etc.), scripts for data loading and preprocessing, and evaluation routines (e.g. accuracy, error metrics). Because it is structured as homework or practice material, the code is likely intended more for didactic use than for production deployment. ... -
22
Python Machine Learning
The "Python Machine Learning (2nd edition)" book code repository
This repository accompanies the well-known textbook “Python Machine Learning, 2nd Edition” by Sebastian Raschka and Vahid Mirjalili, serving as a complete codebase of examples, notebooks, scripts and supporting materials for the book. It covers a wide range of topics including supervised learning, unsupervised learning, dimensionality reduction, model evaluation, deep learning with TensorFlow, and embedding models into web apps. Each chapter has Jupyter notebooks and Python scripts that... -
23
RecNN
Reinforced Recommendation toolkit built around pytorch 1.7
This is my school project. It focuses on Reinforcement Learning for personalized news recommendation. The main distinction is that it tries to solve online off-policy learning with dynamically generated item embeddings. I want to create a library with SOTA algorithms for reinforcement learning recommendation, providing the level of abstraction you like. -
24
CCZero (中国象棋Zero)
Implement AlphaZero/AlphaGo Zero methods on Chinese chess
ChineseChess-AlphaZero is a project that implements the AlphaZero algorithm for the game of Chinese Chess (Xiangqi). It adapts DeepMind’s AlphaZero method—combining neural networks and Monte Carlo Tree Search (MCTS)—to learn and play Chinese Chess without prior human data. The system includes self-play, training, and evaluation pipelines tailored to Xiangqi's unique game mechanics. -
25
lazynlp
Library to scrape and clean web pages to create massive datasets
LazyNLP is a lightweight tool for collecting and curating large-scale text datasets for machine learning and NLP applications with minimal manual effort.
