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System information

• TensorFlow version (you are using): 2.1
• Are you willing to contribute it (Yes/No): No

Please implement the latest examples in the examples/ folder to the docs/mkdocs.yml to see theses examples on the homepage. Perhaps a subsectioning for that many examples is necessary. See Homepage

Thank you!

Describe the issue linked to the documentation

The docs for PolynomialFeatures.fit() does not mention that sparse data is allowed. The same holds true for fit_transform.

Suggest a potential alternative/fix

The docs for `PolynomialFeatur

Zeros and tensor printing for float and complex

1. In float tensor print, the trailing zeros ( with precision 4) are printed only when there’s one or more digit after decimal or otherwise just a decimal is printed
2. In complex tensor print rn, the trailing zeros are printing in all cases

torch.zeros((2,2), dtype=torch.float64)
tensor([[0., 0.],
[0., 0.]], dtyp

https://github.com/aymericdamien/TensorFlow-Examples/blob/9e1bb504f5d0f209d000997ce2ad95bb891798ab/examples/3_NeuralNetworks/bidirectional_rnn.py#L81

I think "outputs [-1]" and "outputs [0]" are equivalent (reversed) in this line of code, but the former (89%) works better than the latter (86%). Why?

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.

• face_recognition version: latest docs
• Python version: N/A
• Operating System: N/A

Description

the docs say that default value for 'model' is "large". in fact, it is "small", which makes a different. please correct the docs

Could someone tell me if this example comes with any documents about preparing data, training, and testing? Thanks.

Describe the bug
can't find "from sklearn.cross_validation import train_test_split" in Latest version scikit-learn
To Reproduce
Steps to reproduce the behavior:

• Day1
• Step 5: Splitting the datasets into training sets and Test sets
• Can't find "from sklearn.cross_validation import train_test_split" in Latest version scikit-learn**

**Desktop (please complete the following infor

I believe methods should have a bit more of information, consider,

struct A end
methods(A)
struct B{T} end
methods(B)
methods(B{Int})
struct C{T}
    C(x) = new{Int}(0)
end
methods(C)

Basically the way that methods are collected / match isn’t well documented.

This should really help to keep a track of papers read so far. I would love to fork the repo and keep on checking the boxes in my local fork.

For example: Have a look at this section. People fork this repo and check the boxes as they finish reading each section.

In Transformation Pipeline make class DataFrameSelector for custom transformation and call DataFrameSelector(num_attribs) it show
TypeError: object() takes no parameters
and same with CombinedAttributesAdder
i m using colab

from sklearn.base import BaseEstimator , TransformerMixin
class DataFrameSelector(BaseEstimator,TransformerMixin):
  def _init_(self,attribute_names):
  

The current installation doc (https://xgboost.readthedocs.io/en/latest/build.html) has accumulated lots of sections over time. It's time to simplify it. I want to make it look simple and easy to use.

Good examples:

• RAPIDS: https://rapids.ai/start.html
  

"Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easi

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.

I was going though the existing enhancement issues again and though it'd be nice to collect ideas for spaCy plugins and related projects. There are always people in the community who are looking for new things to build, so here's some inspiration ✨ For existing plugins and projects, check out the spaCy universe.

If you have questions about the projects I suggested,