I think we need more detailed description and example for streaming training data from disk on
https://www.tensorflow.org/guide/data#basic_mechanics.

It is mentioned on other documents, but no how-tos. It would be helpful if we add how to implement streaming data from disk and improvements on TF2.0, if any.

When iterating over training data that fits in memory, feel free to use regular Py

The following do not yet have any pytests:

• boolean_algebra
• digital_image_processing
• divide_and_conquer
• hashes
• linear_algebra #1124
• networking_flow #1126
• traversals
• file_transfer_protocol # This might be difficult
• machine_learning
• neural_network

For ease of review, please only modify one directory per pull request.

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

Proposing to bundle Caltech-101 dataset in the next release of Keras (or any future release) as a standard dataset for image-related application tasks.

It is small (compared to the whole ImageNet) but more difficult and challenging to train and experiment on than MNIST or CIFAR-10.

It was the precursor to Dr. Fei Fei Li's ImageNet p

Brew is listed as the recommended way of installing httpie on macOS. However, it doesn't work with auth plugins.

For example, if you pip3 install requests-hawk and then run http --help hawk will not show as an auth type. If you pip3 install httpie-oauth it will install httpie via pip as a dependency and overwrite the brew installed link in /usr/local/bin/http and now all the plugins wil

tldr; in multi-process environment (Celery) sessions might lead to request/responses being mixed up.

It is unsafe to use Session in a multi-process environment - if the fork happens after Session initialisation the underlying connection pool will be shared across both processes, leading to potentially dangerous and hard to debug issues.

I'm not sure what should happen - whather a code change

SUMMARY

The module documentation uses the syntax C(code snippet) for short code quotations. When a lower-case c is used by mistake, the content does not display correctly.

Find and fix instances of c(code snippet) in the module documentation. For example: https://github.com/ansible/ansible/blob/devel/lib/ansible/modules/cloud/google/gcp_cloudbuild_trigger.py#L327.

ISSUE TY

We recently merged https://github.com/NicolasHug/scikit-learn/blob/master/examples/release_highlights/plot_release_highlights_0_22_0.py to summarise the highlights of 0.22, to be released in early November.

More release highlights, briefly demonstrating the new thing in action, belong in that example doc:

• Permutation based feature importance
• HistGradientBoostingClassifier with m

This involves, among other things:

• Enabling a Python 3.8 environment in Tox
• Enabling a Python 3.8 job in the Travis CI
• Updating the setup.py file

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.

The command certbot register --update-registration has been deprecated for a long time in favor of certbot update_account. Let's remove --update-registration and the code that uses it.

There are issues in some docstrings where the See Also section is not complete. All of the following errors exist because there is no description for a referenced function or method. If these can get fixed then CI will be able to check all of the See Also section errors! The command to check for the errors is ./scripts/validate_docstrings.py --errors=SA04

pandas.melt: Missing description

能不能找出一个九位数，这个数包含了1-9这九个数字，并且这九个数的前n位能被n整除，若这个数为abcdefghi，则ab可以被2整除，abc可以被3整除... abcdefghi可以被9整除。

请尽量优化你的复杂度，包括但不限于剪枝