tf.function makes invalid assumptions about arguments that are Mapping instances. In general, there are no requirements for Mapping instances to have constructors that accept [(key, value)] initializers, as assumed here.

This leads to cryptic exceptions when used with perfectly valid Mapping s

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.

It says in the documentation (the last section - "Working with Virtual Environments"):

For Python 3 add the following lines to the top of your .wsgi file:

activate_this = '/path/to/env/bin/activate_this.py'
with open(activate_this) as file_:
    exec(file_.read(), dict(__file__=activate_this))

However `activate_this.p

I was going through the docs in

examples/README.md

and many of the links there are broken which would cause problem to the people are new and don't know about the scripts.

I would like to help on this :))

In = syntax,

• double quotes (")
• back slashes (\)
• non-ascii characters

are escaped in json.dumps:

$ http -v httpbin.org/post                                      \
    dquote='\"'                                                 \
    multi-line='line 1\nline 2'                               

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

In the IterativeImputer, min_value and max_value are defaulted to None. Internally, if they are None min and max value will be affected to -np.inf and np.inf, respectively.

We should change this behaviour and make that the default of min_value=-np.inf and max_value=np.inf directly.

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?

🐛 Bug

To Reproduce

Run following from jupyter lab console

import torch
foo = torch.arange(5)
foo.as_strided((5,), (-1,), storage_offset=4)

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/daniil/.local/lib/python3.6/site-packages/torch/tensor.py", line 159, i

They offer a simpler syntax and speed, so once we drop Python 3.5 support I guess it makes sense to look into replacing % and format usages with f-strings where possible.

• 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

Description

The instructor in the above mentioned video has created a new version of the same tutorial, which can be found here

Why

It is always good to keep resources and tutorials up-to-date. The new video talks about namespaces, chroot and cgroups, and speaks about containers at a greater depth.

Is this something you're interest

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

假如有一排房子，共 n 个，每个房子可以被粉刷成红色、蓝色或者绿色这三种颜色中的一种，你需要粉刷所有的房子并且使其相邻的两个房子颜色不能相同。

当然，因为市场上不同颜色油漆的价格不同，所以房子粉刷成不同颜色的花费成本也是不同的。每个房子粉刷成不同颜色的花费是以一个 n x 3 的矩阵来表示的。

例如，costs[0][0] 表示第 0 号房子粉刷成红色的成本花费；costs[1][2] 表示第 1 号房子粉刷成绿色的花费，以此类推。请你计算出粉刷完所有房子最少的花费成本。

注意：

所有花费均为正整数。

示例：

输入: [[17,2,17],[16,16,5],[14,3,19]]
输出: 10
解释: 将 0 号房子粉刷成蓝色，1 号房子粉刷成绿色，2 号房子粉刷成蓝色。
  最少花费: 2 + 5 + 3 = 10。

题目地址： ht

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For the sake of killing certbot-auto and doing it gradually, I would like to start deprecating the script on more systems. For systems where snaps are supported, my current inclination is to wait until we think our snaps are production ready and then direct them to use the snaps.

For systems where snaps cannot be used and we have other installation instructions available, I think it may be wort

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 #28854, the note

In the current implementation applymap calls func twice on the first column/row to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if func has side-effects, as they will take effect twice for the first column/row.

was removed from the DataFrame.apply docstring.

However, it is still in the [docstring](https://pandas.pydata

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