URL with the issue: https://www.tensorflow.org/api_docs/python/tf/nn/ctc_loss

Description of issue:

There's no example provided for using this loss and I cannot make it work.
Following the parameters definitions I created this toy example in tf2.0.0:

import functools
import tensorflow as tf
import numpy as np
from tensorflow.keras.models import Model
from tensorflow.keras.la

In the solution for the pastebin/bitly system design, the write to cache flow is missing in the second diagram which refers to the scaling aspect. The cache has only a read arrow but not a write arrow. The diagram will be more intuitive if a write-through/write-back cache mechanism was indicated.

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

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'                               

Looks like www.python-requests.org and docs.python-requests.org are redirecting to https://2.python-requests.org and failing SSL negotiation, making the site appear down. Google links are all dead, documentation links don't work, etc.

http://2.python-requests.org redirects to https://requests.kennethreitz.org/en/master/, which works.

Expected Result

Website should appear or redirect to

SUMMARY

- include_tasks: included.yml
  loop:
    - 1
    - 2

Expected output:

TASK [include_tasks] ******************************
included: …/included.yml for localhost => (item=1)
included: …/included.yml for localhost => (item=2)

Current output:

TASK [include_tasks] ******************************
included: …/included.yml for localhost
included: …/in

Description

ValueError: Unknown label type: 'unknown' thrown when passing sparse matrix y in RandomForestClassifier.fit.

The reason is that several numpy functions are called on the variable:

• atleast_1d https://github.com/scikit-learn/scikit-learn/blob/f65cdf2d5e5f2b753393190598f9c9f63f34dbe1/sklearn/ensemble/_forest.py#L307
• copy https://github.com/scikit-learn/scikit-learn/bl

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?

This is not an issue related with the code itself but with Scrapy.

I've seen that the only Wikipedias with the Scrapy entry are:

• English: https://en.m.wikipedia.org/wiki/Scrapy

• French: https://fr.m.wikipedia.org/wiki/Scrapy

• Polish: https://pl.m.wikipedia.org/wiki/Scrapy

• Chinese: https://zh.m.wikipedia.org/wiki/Scrapy

I think it could be a good idea to create this issue

Currently, PyTorch C++ API is missing many torch::nn layers that are available in the Python API. As part of the Python/C++ API parity work, we would like to add the following torch::nn modules and utilities in C++ API:

Containers

• Module (TODO: some APIs are missing in C++, e.g. register_forward_hook / register_forward_pre_hook)
• Sequential (@ShahriarSS)
• Modul

• face_recognition version: 1.2.3
• Python version: 3.6
• Operating System: OSX

Description

I've been experimenting with using SVMs for working with multiple faces per person, based on the example for SVM here.

Here are the challenges I am facing:

• using clf.predict ([ref](https://gith

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

One of our packagers asked if we could host our PGP somewhere accessible over HTTPS so they could automatically download it for extra verification on our PyPI packages. I think we can do this.

If this is done, we should document the existence of this file in https://github.com/certbot/certbot/blob/master/certbot/docs/packaging.rst.

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

项目推荐

• 项目地址：https://github.com/nushell/nushell

• 类别：Rust

• 项目后续更新计划：
  该项目已达到最低可行的产品质量水平。虽然贡献者将它作为日常驱动程序，但它可能对某些命
  令不稳定。未来版本将填补缺失的功能并提高稳定性。它的设计也随着成熟而变化。

  Nu附带了一组内置命令（如下所示）。如果命令未知，命令将弹出并执行它（在 Windows 上使
  用 cmd 或在 Linux 和 MacOS 上使用 bash），正确地通过 stdin，stdout 和 stderr，所以像你的日常 git 工作流程甚至 vim 可以正常工作。

  还有一本关于 Nu 的书，目前正在进行中。

• 项目描述：这是一个 Github 时代下，一个更加现代的 shell。Nushell 将 shell 命

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

https://github.com/pandas-dev/pandas/blob/62a87bf4a2af02a8d3bc271ad26e5994292b8e6a/pandas/io/pickle.py#L95

read_pickle does not accept google storage URL (in form "gs://bucket-name/path/file.pkl") as input

While this code works fine:
with tf.io.gfile.GFile("gs://bucket-name/path/file.pkl", "rb") as infile: df = pd.read_pickle(infile, compression=None)

Might be reasonable to

I was having a very hard time figuring out

fill = A.stack().mean()
A.add(B, fill_value=fill)

fill = 4.5. However I computed a value of 3.2 because I was taking the mean from the column of A not the DataFrame A.
This coming after the Indexing chapter where "explicit is better than implicit." I was thinking that this should be a little more explicit.