Difference between revisions of "Datasets"
| Line 28: | Line 28: | ||
* [http://registry.opendata.aws/ Registry of Open Data on AWS | Amazon] | * [http://registry.opendata.aws/ Registry of Open Data on AWS | Amazon] | ||
* [http://www.google.com/publicdata/directory Public Data | Google] | * [http://www.google.com/publicdata/directory Public Data | Google] | ||
| + | * [http://cloud.google.com/bigquery/public-data/ BigQuery public datasets | Google] | ||
* [http://storage.googleapis.com/openimages/web/index.html Open Images | Google] | * [http://storage.googleapis.com/openimages/web/index.html Open Images | Google] | ||
* [http://www.microsoft.com/en-us/research/academic-program/data-science-microsoft-research/ Data Science for Research | Microsoft] | * [http://www.microsoft.com/en-us/research/academic-program/data-science-microsoft-research/ Data Science for Research | Microsoft] | ||
Revision as of 11:26, 20 April 2019
YouTube search... ...Google search
- Batch Norm(alization) & Standardization
- Data Preprocessing
- Feature Exploration/Learning
- Hyperparameters
- Data Augmentation
- Visualization
- Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog
Datasets (often in combination with algorithms) are becoming more important themselves and can sometimes be seen as the primary intellectual output of the research. The revelations about Cambridge Analytica highlights the importance of datasets and data collection. Reference also: Privacy in Data Science
Sources
- Kaggle Datasets
- UC Irvine Machine Learning Repository
- MNIST database
- Collections | DataHub
- Registry of Open Data on AWS | Amazon
- Public Data | Google
- BigQuery public datasets | Google
- Open Images | Google
- Data Science for Research | Microsoft
- Datasets for Data Mining and Data Science | KDnuggets
- Enigma Public
- A Comprehensive List of Open Data Portals from Around the World | DataPortals.org
- OpenDataSoft
- World Data Atlas | Knoema
- The Open Machine Learning project | OpenML.org
- World's Free Online Data | Research Pipeline
- List of datasets for machine learning research | Wikipedia
- Neural Net Repository | Wolfram
- Open Data for Deep Learning & Machine Learning | 4j
- Data Catalog | Data.gov
- Wind Turbine Map and Database | USGS & DOE
- Autosomal DNA
- Pascal Visual Object Classes Challenge (VOC)
- OpenNASA
- JASA Data Archive | Journal of the American Statistical Association
- Datasets Archive | Journal of the American Statistical Association
- Data.World
- The Dataset Collection | Archive.org
- Collections |Archive-it.org
- Eurostat | EU statistical office
- Re3data
- Resource on data and metadata standards - open research data | FAIRsharing
- List of Public Data Sources Fit for Machine Learning | bigml
- Open Datasets | Skymind
- Global Health Observatory resources | World Health Organization (WHO)
- CDC WONDER | Center for Disease Control (CDC)
- US health insurance program | Medicare
- International economy |International Monetary Fund (IMF)
- Data Catalog }| The World Bank
- Financial and economic | Quandl
- PublicDomains | GitHub
- datasets and related content | BuzzFeed - GitHub
- Sports, politics, economics, and other spheres of life | FiveThirtyEight
- EMBER; benign and malicious Windows-portable executable files | Endgame - GitHub
- r/datasets | reddit
- Microsoft Information-Seeking Conversation (MISC) - audio and video signals; transcripts of conversation
Articles
- The 50 Best Free Datasets for Machine Learning | Meiryum Ali - Gengo AI
- The 50 Best Public Datasets for Machine Learning | Stacy Stanford - Medium
- Best Public Datasets for Machine Learning and Data Science: Sources and Advice on the Choice | Altexsoft
- 25 Open Datasets for Deep Learning Every Data Scientist Must Work With | PRANAV DAR - Analytics Vidhya
- Human in the Loop...
- Amazon Mechanical Turk (MTurk) - Using MTurk with Amazon SageMaker for Supervised Learning (ML)
- Gengo.ai - high-quality multilingual data with a human touch for machine learning
- Figure Eight CrowdFlower AI - build a state-of-the-art machine learning model trained with human labeled data
