URL(s) with the issue:

https://www.tensorflow.org/tfx/tutorials/transform/census#python_check_imports_and_globals

Description of issue (what needs changing):

The text says "First we'll make sure that we're using Python 2, and then go ahead and install and import the stuff we need." but the code below indicates we need to use Python 3.

Clear description

The text states:

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. "

Hey,

I'm new to github and thought this repo would be a good opportunity to get familiar and contribute a simple algorithm. I noticed the sorts folder is lacking a recursive implementation of the insertion sort. Is it OK if I add one?

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.

Expected Behavior

Missing templates should raise a TemplateNotFound exception with the correct template name.

Actual Behavior

The top-level template is used as the error string even when it is not the template that failed.

There appears to be an issue in Flask's DispatchingJinjaLoader. If a template called parent.html fails to find a sub-template such as `{% extends child

In the given documentation, the mentioned key are acc and val_acc, but actually it is accuracy and val_accuracy.

Given documentation screenshot:

Whereas the actual keys are `dict_keys(['val_loss', 'val_accuracy

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'                               

SUMMARY

Since 2.9 mysql_db dump create dump with create database and use instruction.
Is that possible to add an option for using --no-create-db with a dump ?

ISSUE TYPE

• Feature Idea
• Bug Report

COMPONENT NAME

When i import requests and run help(requests), Output is this:

ESCRIPTION
Requests HTTP Library
~~~~~~~~~~~~~~~~~~~~~

Requests is an HTTP library, written in Python, for human beings. Basic GET
usage:

   >>> import requests
   >>> r = requests.get('https://www.python.org')
   >>> r.status_code
   200
   >>> 'Python is a programming lang

Description

if MultinomialNB there is strange behavior of clf.coef_:
clf.coef_ is the same as clf.feature_log_prob_[1]

and

clf.intercept_ is the same as only one clf.class_log_prior_

for example
clf.feature_log_prob_[0][0:3]

array([-3.63942161, -3.17296199, -4.59417863])

clf.feature_log_prob_[1][0:3]

array([-3.51935008, -3.010937 , -6.41836494])

clf.coef_[0][0:3]

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb

trainable_variables = weights.values() + biases.values() doesn't work.
Also if I write trainable_variables = list(weights.values()) + list(biases.values()), I have to turn on tf.enable_eager_execution(), but the training result is wrong, accuracy is ar

Versions Affected:

All versions after PR #2082 when flags were added to Request. (>1.4.0)

Response.follow() method not consistent with Request.init(). Missing flags.

No sure how this got missed since PR #2082 was merged, but looking at the parameters for Request.__init__ and Response.follow it appears flags wasn't added to Response.follow to keep it in line with ho

Context

We would like to add torch::nn::functional::normalize to the C++ API, so that C++ users can easily find the equivalent of Python API torch.nn.functional.normalize.

Steps

• Add torch::nn::NormalizeOptions to torch/csrc/api/include/torch/nn/options/normalization.h (add this file if it doesn’t exist), which should include the following parameters (based on https://pytorch.

• face_recognition version:4.1.0
• Python version:3.7
• Operating System:windows 10

Description

I'm having problem to run this program, the error under below

gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
cv2.error: OpenCV(4.1.0) C:\projects\opencv-python\opencv\modules\imgp

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

Target Leakage in mentioned steps in Data Preprocessing. Train/test split needs to be before missing value imputation. Else you will have a bias in test/eval/serve.

Python 3.9 is planning on enforcing that abstract base classes in the collections module are imported from collections.abc. See https://docs.python.org/3/library/collections.html#module-collections.

We currently are not always doing this in Certbot and are ignoring the deprecation warning about it at https://github.com/certbot/certbot/blob/6e07e8b5c0f3c6da2ae03a2a63adc7ce8e15a202/pytest.ini#L

给定两个由小写字母构成的字符串 A 和 B ，只要我们可以通过交换 A 中的两个字母得到与 B 相等的结果，就返回 true ；否则返回 false 。

示例 1：

输入： A = "ab", B = "ba"
输出： true
示例 2：

输入： A = "ab", B = "ab"
输出： false
示例 3:

输入： A = "aa", B = "aa"
输出： true
示例 4：

输入： A = "aaaaaaabc", B = "aaaaaaacb"
输出： true
示例 5：

输入： A = "", B = "aa"
输出： false
 

提示：

0 <= A.length <= 20000
0 <= B.length <= 20000
A 和 B 仅由小写字母构成。

来源：力扣（L

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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

Code Sample, a copy-pastable example if possible

dataframe = pd.DataFrame({'x': [1, 2, 3], 'y': ['a', 'b', 'c']})
series = dataframe.stack()
print(series.to_json(orient="index"))

The keys in the output have unescaped quotes in them, resulting in invalid JSON. Output here is indented for clarity:

{
    "[0,"x"]": 1,
    "[0,"y"]": "a",
    "[1,"x"]": 2,

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