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.
If you are new to Intel DAAL, start with the summary of the product functionality.
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:
Intel® DAAL provides application programming interfaces for C++, Java*, and Python* languages. Visit Intel® Data Analytics Acceleration Library API Reference to download API References for C++, Java*, and Python*. API Reference for C++ is also available online on IDZ, see C++ API Reference for Intel® Data Analytics Acceleration Library.