-
Updated
Jan 1, 2021 - Python
machine-learning-library
Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.
Here are 191 public repositories matching this topic...
-
Updated
Dec 30, 2020 - C++
-
Updated
Dec 24, 2020 - Python
https://igel.readthedocs.io/en/latest/_sources/readme.rst.txt includes a link to the assets/igel-help.gif, but that path is broken on readthedocs.
readme.rst is included as ../readme.rst in the sphinx build.
The gifs are in asses/igel-help.gif
The sphinx build needs to point to the asset directory, absolutely:
.. image:: /assets/igel-help.gif
I haven't made a patch, because I haven't
-
Updated
Jun 5, 2019 - Python
-
Updated
Dec 27, 2020 - Python
I'm sorry if I missed this functionality, but CLI
version hasn't it for sure (I saw the related code only in generate_code_examples.py
). I guess it will be very useful to eliminate copy-paste phase, especially for large models.
Of course, piping is a solution, but not for development in Jupyter Notebook, for example.
-
Updated
Dec 29, 2020 - Python
-
Updated
Jul 17, 2020 - Python
String representations of dataset objects are used for previewing their contents from the terminal. When converting a Dataset object to a string, we build a table using ascii characters. The current table has fixed width columns that do not take full advantage of the terminal real estate if the dataset only contains a few columns.
echo $dataset;
<img width="574" alt="Annotation
-
Updated
Dec 9, 2020 - Python
-
Updated
Jan 1, 2021 - Python
-
Updated
Oct 12, 2020 - Java
-
Updated
May 30, 2020 - Python
-
Updated
Aug 29, 2016 - Python
-
Updated
Sep 23, 2020 - Python
-
Updated
Jun 14, 2017 - Python
-
Updated
Oct 18, 2020 - JavaScript
-
Updated
Oct 28, 2018
-
Updated
Oct 27, 2020 - Python
-
Updated
Dec 27, 2020 - Python
-
Updated
Dec 22, 2020 - Java
-
Updated
Oct 13, 2020 - Java
-
Updated
Aug 28, 2018 - Swift
-
Updated
Aug 30, 2020 - Jupyter Notebook
-
Updated
Dec 21, 2020 - Java
-
Updated
Jul 1, 2019 - Python
-
Updated
Dec 23, 2020 - Python
-
Updated
Dec 4, 2020 - R
- Wikipedia
- Wikipedia
This is a follow-up for mlpack/mlpack#2647. Based on the experiments in mlpack/mlpack#2777 we figured it makes sense to remove the boost::variant approach. This should not only make it easier for new-comers to contribute to the network codebase (the boost::variant and visitor approach can be confusing) but also reduce the memory usage during the bu