mkl
Here are 56 public repositories matching this topic...
-
Updated
May 13, 2020 - Go
-
If you discover a bug or regression in either the code or documentation, please report it. We appreciate your time to make the bug report.
- Please provide a small and self-contained program which exposes the bug. The small program should have a
main()function and use only functions/classes
- Please provide a small and self-contained program which exposes the bug. The small program should have a
-
Updated
Feb 14, 2020 - C++
I just noticed that export_wisdom etcetera are not documented.
Also, these implementations are kind of old … it would be good to update them to support e.g. exporting to a String, an io::IO, etcetera.
-
Updated
Nov 9, 2019 - Shell
-
Updated
Jul 10, 2020 - C++
-
Updated
Jun 14, 2020 - Python
Describe the bug
If eps_r is shape (N,) then the fields solved are shape (N,1)
Either:
- disallow 1-D
eps_rarrays
Or: - keep track of
eps_rshape and reshape the fields to match.
In the Observations section of README.md, it is stated that:
As expected Armadillo with internal BLAS/LAPACK wrappers performance is pretty poor.
This is incorrect, as the meaning of the "internal wrapper" is wrong. There is either a wrapper for an external library, or there isn't. A re-implementation of a function is not an "internal" wrapper, but simply internal functionality. The corr
-
Updated
Apr 2, 2018 - Python
-
Updated
Oct 2, 2017 - Makefile
-
Updated
Jun 18, 2020 - C++
-
Updated
Jul 6, 2020 - Julia
-
Updated
May 28, 2020 - Python
-
Updated
Dec 14, 2018 - Python
-
Updated
May 28, 2020 - Python
-
Updated
Jul 31, 2018 - Fortran
-
Updated
Jul 19, 2018 - Shell
-
Updated
Jul 10, 2020 - Python
-
Updated
Apr 23, 2019 - C#
-
Updated
Jan 11, 2020 - C++
-
Updated
Mar 28, 2019 - C
-
Updated
May 19, 2019
-
Updated
May 30, 2019 - C++
Improve this page
Add a description, image, and links to the mkl topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the mkl topic, visit your repo's landing page and select "manage topics."
I would like to use the skip thought vectors implementation in neon. The documentation provided is not enough. Is there a tutorial available on how to obtain skip thought vectors from scratch on my own documentation ? I have created a virtual environment and downloaded neon. I am not sure how to train the skip thought model. Thanks.