Intel® Data Analytics Acceleration Library (Intel® DAAL) is the library of Intel® architecture optimized building blocks covering all stages of data analytics: data acquisition from a data source, preprocessing, transformation, data mining, modeling, validation, and decision making.
Intel DAAL provides application programming interfaces for C++, Java*, and Python* languages.
Start with the summary of the product functionality.
The library consists of the following major components:
The Data Management component includes classes and utilities for data acquisition, initial preprocessing and normalization, for data conversion into numeric formats done by one of supported Data Sources, and for model representation.
The Algorithms component consists of classes that implement algorithms for data analysis (data mining), and data modeling (training and prediction).
The Services component includes classes and utilities used across Data Management and Algorithms components.
Algorithms implemented in the library support batch, online, and distributed processing modes of computations. More information.
Algorithms implemented in Intel DAAL include:
- Algorithms for analysis:
- Moments of low order and quantiles.
- Correlation and variance-covariance matrices.
- Distance matrices: cosine and correlation.
- K-Means clustering.
- Principal component analysis.
- Matrix decompositions: Cholesky, singular value (SVD), and QR.
- Outlier detection: multivariate and univariate.
- Association rules.
- Sorting observations by features.
- Quality metrics for classification algorithms and linear regression.
- Optimization solvers.
- Normalizations: Z-score and min-max.
- Algorithms for training and prediction:
This document explains basics of programming with Intel DAAL.
You can download Intel® Data Analytics Acceleration Library (Intel® DAAL) application programming interface (API) references from https://software.intel.com/en-us/daal-api-reference.