Difference between revisions of "Data Interoperability"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
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| − | [https://www.youtube.com/results?search_query= | + | [https://www.youtube.com/results?search_query=ai+Data+Interoperability YouTube] |
| − | [https://www. | + | [https://www.quora.com/search?q=ai%20Data%20Interoperability ... Quora] |
| − | [https://www. | + | [https://www.google.com/search?q=ai+Data+Interoperability ...Google search] |
| + | [https://news.google.com/search?q=ai+Data+Interoperability ...Google News] | ||
| + | [https://www.bing.com/news/search?q=ai+Data+Interoperability&qft=interval%3d%228%22 ...Bing News] | ||
| + | * [[Data Science]] ... [[Data Governance|Governance]] ... [[Data Preprocessing|Preprocessing]] ... [[Feature Exploration/Learning|Exploration]] ... [[Data Interoperability|Interoperability]] ... [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] ... [[Bias and Variances]] ... [[Benchmarks]] ... [[Datasets]] | ||
| + | * [[Data Quality]] ...[[AI Verification and Validation|validity]], [[Evaluation - Measures#Accuracy|accuracy]], [[Data Quality#Data Cleaning|cleaning]], [[Data Quality#Data Completeness|completeness]], [[Data Quality#Data Consistency|consistency]], [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]], [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]] | ||
* [https://arrow.apache.org/ Apache Arrow] | * [https://arrow.apache.org/ Apache Arrow] | ||
* [[Python#Pandas | Pandas]] provides an efficient implementation of a DataFrame | * [[Python#Pandas | Pandas]] provides an efficient implementation of a DataFrame | ||
Revision as of 21:05, 1 May 2023
YouTube ... Quora ...Google search ...Google News ...Bing News
- Data Science ... Governance ... Preprocessing ... Exploration ... Interoperability ... Master Data Management (MDM) ... Bias and Variances ... Benchmarks ... Datasets
- Data Quality ...validity, accuracy, cleaning, completeness, consistency, encoding, padding, augmentation, labeling, auto-tagging, normalization, standardization, and imbalanced data
- Apache Arrow
- Pandas provides an efficient implementation of a DataFrame
- Wes McKinney
a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. It also provides computational libraries and zero-copy streaming messaging and interprocess communication. Languages currently supported include C/C++, C#, Go, Java, JavaScript, MATLAB, Python, R Project, Ruby, and Rust.
