Intel® DAAL 2018 is now available. Intel DAAL 2018 packages are now ready for download. Intel DAAL is available as part of the Intel® Parallel Studio XE and Intel® System Studio. Please visit the https://software.intel.com/en-us/intel-daal.
Check Intel® DAAL 2018 release notes to learn more information.
what's New in Intel® DAAL 2018 Update 1:
- Introduced gradient boosted trees algorithm for classification and regression as stochastic gradient boosting machine with regularization and second order numerical optimization in training procedure (xgboost-like) and exact splits mode. The implementation employs multiple levels of parallelization in trees construction and prediction.
- Developed experimental extension library on top of existing pyDAAL package that provides easy to use API for Intel® DAAL neural networks. Extension library allows to configure and train neural network model in few lines of code, and to use existing TensorFlow or Caffe models on inference stage.
- Fixed issue in multi-class classifier so that it now supports other boosting binary classifiers in addition to SVM. Now boosting algorithm clones weak learner before using it, so different threads in multiclass classifier work with different weak learner objects.
- Introduced new experimental distributed k – Nearest Neighbors classifiers for both training and prediction stages. Added new sample which demonstrates how to use this algorithm along with MPI.
- Added support in PCA algorithm for wide matrices (number of rows is less than the number of columns) with correlation method.
- Introduced new feature of optionally calculating results for means and variances of input data set in PCA algorithm. Added support of sign-deterministic output. Library is extended by PCA Transformation algorithm. This feature includes the PCA transformation of dataset with optional data normalization and data whitening. Introduced quality metrics for PCA: explained variances, explained variance ratios and noise variance.
- Introduced new feature of optionally calculating results for means and variances of input data set in Zscore algorithm.
what's New in Intel® DAAL 2018:
- Introduced API modifications to streamline library usage and enable consistency across functionality.
- Introduced support for Decision Tree for both classification and regression. The feature includes calculation of Gini index and Information Gain for classification, and mean squared error (MSE) for regression split criteria, and Reduced Error Pruning.
- Introduced support for Decision Forest for both classification and regression. The feature includes calculation of Gini index for classification, variance for regression split criteria, generalization error, and variable importance measures such as Mean Decrease Impurity and Mean Decrease Accuracy.
- Introduced support for varying learning rate in the Stochastic Gradient Descent algorithm for neural network training.
- Introduced support for filtering in the Data Source including loading selected features/columns from CSV data source and binary representation of the categorical features.
- Extended Neural Network layers with Element Wise Add layer.
- Introduced new samples that allow easy integration of the library with Spark* MLlib.
- Introduced service method for enabling thread pinning: Performance improvements in various algorithms on Intel® Xeon® Processor supporting Intel® Advanced Vector Extensions 512 (Intel®AVX-512) (codename Skylake Server).