diff --git a/docs/source/deprecation.rst b/docs/source/deprecation.rst index 50641ecbd88..78e53b5211c 100644 --- a/docs/source/deprecation.rst +++ b/docs/source/deprecation.rst @@ -18,7 +18,7 @@ Deprecation Notice ================== -This page provides information about the deprecations of a specific oneDAL functionality. +This page provides information about the deprecations of a specific Intel(R) oneAPI Data Analytics Library (oneDAL) functionality. Java* Interfaces **************** diff --git a/docs/source/onedal/algorithms/clustering/kmeans.rst b/docs/source/onedal/algorithms/clustering/kmeans.rst index 899cdf9b988..bbee5da67e0 100644 --- a/docs/source/onedal/algorithms/clustering/kmeans.rst +++ b/docs/source/onedal/algorithms/clustering/kmeans.rst @@ -52,7 +52,7 @@ Expression :math:`\|\cdot\|` denotes :math:`L_2` `norm .. note:: In the general case, :math:`d` may be an arbitrary distance function. Current - version of the oneDAL spec defines only Euclidean distance case. + version of the Intel(R) oneAPI Data Analytics Library (oneDAL) specification defines only Euclidean distance case. .. _kmeans_t_math_lloyd: diff --git a/docs/source/onedal/algorithms/decomposition/pca.rst b/docs/source/onedal/algorithms/decomposition/pca.rst index 4c167538412..7c24d012b36 100644 --- a/docs/source/onedal/algorithms/decomposition/pca.rst +++ b/docs/source/onedal/algorithms/decomposition/pca.rst @@ -76,7 +76,7 @@ The PCA algorithm can be trained using either the covariance or the correlation The choice of covariance matrix or correlation matrix is application-dependent. More specifically, if scaling of the features is important for a problem, which is often the case, using the correlation matrix to compute principal components is more appropriate. -By default, oneDAL uses the correlation matrix to compute the principal components. It is possible +By default, Intel(R) oneAPI Data Analytics Library (oneDAL) uses the correlation matrix to compute the principal components. It is possible to use the covariance matrix by passing "precomputed" as method and feeding a covariance matrix as input to the PCA algorithm. To compute the covariance matrix, the :ref:`Covariance ` algorithm can be used. diff --git a/docs/source/onedal/gpu_support.rst b/docs/source/onedal/gpu_support.rst index 5a8d5f570b2..d985631506a 100644 --- a/docs/source/onedal/gpu_support.rst +++ b/docs/source/onedal/gpu_support.rst @@ -23,7 +23,7 @@ See the differences in CPU and GPU support below. GPU-Supported Targets ********************* -OneDAL is designed to work with Intel(R) GPUs specifically, but it could potentially +Intel(R) oneAPI Data Analytics Library (oneDAL) is designed to work with Intel(R) GPUs specifically, but it could potentially run on other hardware platforms if a SYCL runtime is available. .. tabularcolumns:: |\Y{0.5}|\Y{0.5}| diff --git a/docs/source/onedal/introduction.rst b/docs/source/onedal/introduction.rst index fc08072a8af..7fb3023e306 100644 --- a/docs/source/onedal/introduction.rst +++ b/docs/source/onedal/introduction.rst @@ -16,7 +16,7 @@ Introduction ************ -oneDAL provides redesigned versions of interfaces that account for multi-device targets. +Intel(R) oneAPI Data Analytics Library (oneDAL) provides redesigned versions of interfaces that account for multi-device targets. For example, CPU and GPU, distributed SPMD interfaces, and many more. Algorithms Support diff --git a/docs/source/onedal/spmd/index.rst b/docs/source/onedal/spmd/index.rst index 2e139d54ac5..0deaa961520 100644 --- a/docs/source/onedal/spmd/index.rst +++ b/docs/source/onedal/spmd/index.rst @@ -40,7 +40,7 @@ accordance with the input. :width: 800 :alt: Typical SPMD flow - Example of SPMD Flow in oneDAL + Example of SPMD Flow in Intel(R) oneAPI Data Analytics Library (oneDAL). .. _communicator_operations: