Machine learning
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.
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Please make sure that this is a Bug or a Feature Request and provide all applicable information asked by the template.
If your issue is an implementation question, please ask your question on StackOverflow or on the Keras Slack channel instead of opening a GitHub issue.
**System inf
Not sure when this happened but i love the new left-hand side navigation https://scikit-learn.org/dev/user_guide.html
(@adrinjalali did this maybe?)

However, when clicking the different entries, the result is inconsistent. For some, it shows a TOC that expands the existing toc with m
https://github.com/aymericdamien/TensorFlow-Examples/blob/f6f80833e025ef22cfe9cebd9ca832e33134579f/examples/5_DataManagement/tensorflow_dataset_api.py#L43-L44
https://github.com/aymericdamien/TensorFlow-Examples/blob/f6f80833e025ef22cfe9cebd9ca832e33134579f/examples/5_DataManagement/tensorflow_dataset_api.py#L125-L125
This is not an issue but a question.
It seems the '_data' and _labels fe
wiki getting spammed
Please see https://github.com/tesseract-ocr/tesseract/wiki/4.0-with-LSTM/_history
The content on 4.0-with-LSTM page has been deleted.
There should be some control or moderation of changes to wiki.
I would also suggest using https://github.com/apps/stale bot to prune out inactive\old issues, though number of days of inactivity can be larger than default of 60.
Line 1137 of the Caffe.Proto states "By default, SliceLayer concatenates blobs along the "channels" axis (1)."
Yet, the documentation on http://caffe.berkeleyvision.org/tutorial/layers/slice.html states, "The Slice layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices." which seems to be
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I'm not sure if XGBoost s model is well calibrated with softmax. It would be nice to have a doc with various experiments including random forest, dart etc.
Alexnet implementation in tensorflow has incomplete architecture where 2 convolution neural layers are missing. This issue is in reference to the python notebook mentioned below.
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What's the ETA for updating the massively outdated documentation?
Please update all documents that are related building CNTK from source with latest CUDA dependencies that are indicated in CNTK.Common.props and CNTK.Cpp.props.
I tried to build from source, but it's a futile effort.
Hi,
I thank you very much for your excellent work. However, I endure difficulties understanding your code ; my Java and Python training explain it a bit, but I think it would be great for everyone if it was documented. I mean a short explanation of each - complex - function, its role and its algorithm. I would be especially interested in a documentation of the tracker.
I thank you very much in a
In doc.pyx' s line 590:
if not self.is_parsed:
raise ValueError(Errors.E029)
I can still do a good job of chunking by tokenization and pos tagging only, without the full parse. Also in some languages parse isn't available. This will leave more flexibilities to users. I can comment out this in my copy of spacy, but when I update spacy to a new release, I have to chang
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- Wikipedia
Please make sure that this is a bug. As per our
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we only address code/doc bugs, performance issues, feature requests and
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System information
example script provided in TensorFlow): Yes