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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.

The Tutorial section of the docs refers to to the most recent Flaskr application structure however the Testing Deep Dive section is still referring to an application structure from at least the 0.12 version of the tutorial which assumes an application structure that doesn't involve the app factory. This could certainly confuse a new comer... I mean not me.... I certainly wasn't confus

In the documentation it says:

Turns positive integers (indexes) into dense vectors of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]

Neither this explanation nor this example is very clear. I would suggest replacing this with

Turns positive integers (indexes) into dense vectors of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]

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

SUMMARY

The pip module reports a changed status when Ansible is running in --check mode with the package

When using the url http://docs.python-requests.org/en/latest/, it redirects to https://2.python-requests.org//en/latest/ (notice the extra / before en). This causes a HTTP 404.

Expected Result

The redirect should be to https://2.python-requests.org/en/latest/

Actual Result

HTTP 404

Reproduction Steps

Try to visit the latest en documentation for requests using the

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

import torch
torch.finfo(torch.complex64)
Traceback (most recent call last):
File "", line 1, in
TypeError: torch.finfo() requires a floating point input type. Use torch.iinfo to handle 'torch.finfo'
import numpy
numpy.finfo(numpy.complex64)
finfo(resolution=1e-06, min=-3.4028235e+38, max=3.4028235e+38, dtype=float32)

cc @ezyang @anjali411 @dylanbespa

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

https://twisted.readthedocs.io/en/latest/core/howto/defer-intro.html#inline-callbacks-using-yield says "On Python 3, instead of writing returnValue(json.loads(responseBody)) you can instead write return json.loads(responseBody). This can be a significant readability advantage, but unfortunately if you need compatibility with Python 2, this isn’t an option.".

• 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

对于数组[1, 2, 3, 4] 和 [4, 3, 2, 1] 你的冒泡排序算法是否都能获得不错的效率？

In the past we've talked about trying to keep the list of systems we test on relatively current I still think there's some value in that.

If the tests don't immediately work, especially due to failures in certbot-auto, I think it probably makes sense to close/kick this issue until we've done some combination of deprecate certbot-auto and rewrite the test farm tests.

#33148 introduced an empty_frame fixture in the top level conftest. At the same time, we have a lot of ... = DataFrame() assignments in our tests. We should standardize to use the fixture where appropriate

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 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