Trainingsdaten für KI Anwendungen - eine Übersicht

These datasets are applied for machine learning research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets.[1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce.[2][3][4][5]

Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Flexible Data Ingestion.

Best public datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others.

What is the best image annotation tool? Check out the list of the 13 most popular image annotation tools of 2022 and choose the right software for your needs.

Here is a list of 60 open datasets for machine learning, ranging from highly specific data to Amazon product datasets

6861 datasets • 78932 papers with code.

Explore 65+ best free datasets for machine learning projects. Download quality datasets for ML or NLP projects. Save time and start training your models now.

Collections - high quality data and datasets organized by topic. Data Collections, Climate Change, Economic Data, Geodata, Inflation, Linked Open Data, Machine Learning, Reference Data, World Bank

A complete guide for datasets for deep learning. Here is the list of 25 open datasets for deep learning you should work with to improve your DL skills.

Was ist die Datenpoolsuche?
Die Datenpoolsuche ist eine Suchmaschine für Datenpools.

Sie ermöglicht Nutzern, mithilfe eines einfachen Suchbegriffs Datenpools zu finden, die in Tausenden von Repositories im gesamten Web gehostet werden.

The Earth Observing System Data and Information System is a key core capability in NASA’s Earth Science Data Systems Program. It provides end-to-end capabilities for managing NASA’s Earth science data from various sources—satellites, aircraft, field measurements, and various other programs.

This registry exists to help people discover and share datasets that are available via AWS resources. See recent additions and learn more about sharing data on AWS.

Explore the datasets in Azure Open Datasets.

The FBI's Crime Data Explorer (CDE) aims to provide transparency, create easier access, and expand awareness of criminal, and noncriminal, law enforcement data sharing; improve accountability for law enforcement; and provide a foundation to help shape public policy with the result of a safer nation. Use the CDE to discover available data through visualizations, download data in .csv format, and other large data files.

data.world's cloud-native data catalog makes it easy for everyone—not just the "data people"—to get clear, accurate, fast answers to any business question.

Explore more than two petabytes
of open data from particle physics!

The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited "papers" in all of computer science. The current version of the web site was designed in 2007 by Arthur Asuncion and David Newman, and this project is in collaboration with Rexa.info at the University of Massachusetts Amherst. Funding support from the National Science Foundation is gratefully acknowledged.

Many people deserve thanks for making the repository a success. Foremost among them are the donors and creators of the databases and data generators. Special thanks should also go to the past librarians of the repository: David Aha, Patrick Murphy, Christopher Merz, Eamonn Keogh, Cathy Blake, Seth Hettich, and David Newman.

Here you will find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more.

A topic-centric list of HQ open datasets. Contribute to awesomedata/awesome-public-datasets development by creating an account on GitHub.