Please make sure that this is a feature request. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:feature_template

System information

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

A description is incomplete. It should mention:

These patterns are not competing, but complementing each other. To achieve availability, one needs both fail-over and replication.

right after

"There are two main patterns to support high availability: fail-over and replication. "

There are some interesting algorithms in simulation from Physics, Chemistry, and Engineering especially regarding Monte Carlo simulation: Heat Bath algorithm, Metro-Police algorithm, Markov Chain Monte Carlo, etc.

Huge and nice collection and also getting very much appreciated from the community.
It would be great if somebody can translate into English then it will be reaching out to global.

I pulled the latest version of flask Flask from the master branch, commit 64ba43411fbfb2e435455b0df2453d7809b7c5a8.

The Quickstart minimal example on the dev version of Flask does not run as intended. Previous versions of the Quickstart example would display a simple "Hello world" message, but the addition of an undefined "name" variable causes the example to fail with a NameError.

It seems

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!

It's not clear from the website's documentation, or the --help output, how to do the following equivalent curl task:

Post a raw JSON query to ElasticSearch:

curl \
    --header "Content-Type: application/json" \
    --request POST \
    --data '{ "_source": [ "restricted_countries.*" ], "query": { "match_all": {} }, "size": 1000 }' \
    'http://localhost:9200/_search'

T

We're trying to fix as many broken links as possible before modules move into collections. This is the batch of broken links on some Ansible modules.

NOTE: the link checker sometimes reports an error where a link actually works. Ignore those if you find them.

ISSUE TYPE

• Documentation Report

COMPONENT NAME

It could help potential new contributors if the "How to Contribute" section included the content or a link to the "Good Bug Reports" section of the contributing.md and a link to some simple example tests in step 4

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?

After removing the Python 2.7 support, this section:

https://docs.scrapy.org/en/latest/topics/leaks.html#debugging-memory-leaks-with-guppy

should be removed or merged with this:
https://docs.scrapy.org/en/latest/topics/leaks.html#topics-leaks-muppy

• 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

The chat tutorial listed for elixir is outdated. It uses a old version of phoenix and could be confusing to new users who would be learning a oudated set of syntax, application structure, commands i.e. mix phoenix.create vs. mix phx.create.

Also it's missing some steps. For one this they never do mix ecto.create without which the postgres database is not initialized so the app doesn't wo

使用队列实现栈的下列操作：

push(x) -- 元素 x 入栈
pop() -- 移除栈顶元素
top() -- 获取栈顶元素
empty() -- 返回栈是否为空
注意:

你只能使用队列的基本操作-- 也就是 push to back, peek/pop from front, size, 和 is empty 这些操作是合法的。
你所使用的语言也许不支持队列。 你可以使用 list 或者 deque（双端队列）来模拟一个队列 , 只要是标准的队列操作即可。
你可以假设所有操作都是有效的（例如, 对一个空的栈不会调用 pop 或者 top 操作）。

来源：力扣（LeetCode）
链接：https://leetcode-cn.com/problems/implement-stack-using-queues
著作权归领扣网络所有。商业转载请联系官方授权

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

In Certbot's code there are a number of todos written in the Sphinx syntax described at https://www.sphinx-doc.org/en/master/usage/extensions/todo.html. In the config generated by default with sphinx-quickstart, these todos are shown in our documentation by default.

I think the intended audience for these todo comments is Certbot developers rather than Certbot users. Because of this, I don't

I think listing anti-patterns with some basic reasoning about "why not" is a good idea.

Example - singleton. Although #256 has "won't fix" label

• it is in PRs section, and people (if searching history at all) are searching issues first.
• it was misspelled, Singelton instead of Singleton, therefore impossible to find

Listing most popular anti-patterns (without actual implementation) shou

In #26933, we made the capitalization of titles consistent. For example, a title used to be capitalized like, "This is the Section Title", and many of the titles in the pandas documentation was changed to a correct format, like "This is the section title".

In #31114, we made a script called scripts/validate_rst_title_capitalization.py that extracts all titles in the documentation, making s

In the PCA section there is the following quote:

We see that these 150 components account for just over 90% of the variance.

While not inaccurate (150 componen