URL(s) with the issue:

Please provide a link to the documentation entry, for example:
Doc Link:
https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/LearningRateScheduler
Code Link:
https://github.com/tensorflow/tensorflow/blob/r2.0/tensorflow/python/keras/callbacks.py#L1311-L1358

Description of issue (what needs changing):

Parameters

The next API for scheduler

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?

Repro:

@torch.jit.script
class Timebase:
    def __init__(
        self,
        numerator,  # type: int
        denominator,  # type: int
    ):
        # type: (...) -> None
        self.numerator = numerator  # type: int
        self.denominator = denominator  # type: int

Produces the error:

RuntimeError: Return type line '# type: (...) -> ...' not found on multiline 

• face_recognition version: 1.2.3
• Python version: 3.7
• Operating System: Pop!_OS 19.04
• CUDA + CuDNN version: 10.0

I'm trying to use the facerec_from_webcam_multiprocessing.py example with cuda support, but everytime I run it I get this error:

Width: 640, Height: 480, FPS: 30
Process Process-5:
Traceback (most recent call last):
  File "/usr/lib/python3.7/multiprocessing/proce

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.

给定一个 N 叉树，找到其最大深度。

最大深度是指从根节点到最远叶子节点的最长路径上的节点总数。

例如，给定一个 3叉树 :

我们应返回其最大深度，3。

说明:

树的深度不会超过 1000。
树的节点总不会超过 5000。

来源：力扣（LeetCode）
链接：https://leetcode-cn.com/problems/maximum-depth-of-n-ary-tree
著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。

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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