Check out the new and the latest Intel® Data Analytics Acceleration Library (Intel® DAAL) 2017 release! The open source Intel DAAL project is also updated accordingly.
What's new in Intel DAAL:
- Introducing Python programming language API
- Introducing Neural Networks functionality
- Uniform and Xavier initialization methods
- Layers
- Two-dimensional convolutional
- One-, two-, and three-dimensional max pooling
- One-, two-, and three-dimensional average pooling
- Spatial pyramid pooling, stochastic pooling and locally connected layers
- Fully connected
- Dropout
- Logistic
- Hyperbolic tangent
- Rectifier Linear Unit (ReLu)
- Parametric Rectifier Linear Unit (pReLu)
- Smooth Rectifier Linear Unit (smooth ReLu)
- Softmax with cross-entropy loss
- Absolute value (abs)
- Batch normalization
- Local response normalization
- Local contrast normalization
- Concat
- Split
- Optimization solvers
- Stochastic gradient descent
- Mini-batch stochastic gradient descent
- Stochastic limited memory Broyden–Fletcher–Goldfarb–Shanno (lBFGS)
- Mini-batch Adagrad optimization solver
- Objective functions
- Mean squared error (MSE)
- Tensor: Support multiple data layouts, axes control, and computation of tensor size
- Other: Support for user-defined memory allocation to store layer results in Neural Networks
- Added Ridge Linear regression algorithm in batch/online/distributed processing mode
- Added support for quality metrics for linear regression
- Added z-score normalization
- Improved performance for QR, SVD, PCA, variance-covariance, linear regression, Expectation Maximization (EM) for Gaussian Mixture Models (GMM), K-means, and the Naïve Bayes algorithms on the 2nd generation of Intel® Xeon Phi™ processors (codenamed Knights Landing), as well as on the Intel® Xeon® E5-xxxx v3 (codenamed Haswell) and the Intel® Xeon® E5-xxxx v4 (codenamed Broadwell) processors.
- Bug fixes and other improvements in the library and its documentation
Checkout Online Release notes for more information.