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URL(s) with the issue:

Please provide a link

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.

line 123@
X, Y = read_images(DATASET_PATH, MODE, batch_size)
line 66@
classes = sorted(os.walk(dataset_path).next()[1])
StopIteration

Is there a way Tensorflow git cloned repositories can run without overhead issues?

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: 1.2.3
• Python version: 3.7.3
• Operating System: Mac 10.14.4

Description

I wrote a small microservice in which I was getting images in HTTP POST requests and I was trying to recognize them and save their encodings into the database so that no existing face comes again.

It was working fine in my local Mac book environment with the versions written above.

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.

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

When trying to run notebook using binder, every time a cell has import matplotlib.pyplot as plt I get an error:
I was checking in the notebooks that the matplotlib version in this binder is '1.5.1', not sure if that is the problem, but as right now I can't run any notebook that has import matplotlib.pyplot as plt

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