Vectorized version of gradient descent.

theta = theta * reg_param - alpha * (1 / num_examples) * (delta.T @ self.data).T

We should NOT regularize the parameter theta_zero.

theta[0] = theta[0] - alpha * (1 / num_examples) * (self.data[:, 0].T @ delta).T

the first code line ,theta include theta[0].
so I think can write like this:
theta[0] -= alpha * (1 / num_examples) * (self.data[:, 0].

Example scripts contains some dependencies not listed for Horovod, and in some cases require datasets without explaining how to obtain them. We should provide a README file along with a set of packages (requirements.txt) for successfully running the examples.

Is your feature request related to a problem? Please describe.
Other related issues: #408 #251
I trained a Chinese model for spaCy, linked it to [spacy's package folder]/data/zh (using spacy link) and want to use that for ludwig. However, when I tried to set the config for ludwig, I received an error, which tell me that there is no way to load the Chinese model.

ValueError: Key ch

RE: nswfjs.com - Looks like video might have broke and no tests caught it.

Code: https://github.com/infinitered/nsfwjs/tree/master/example/nsfw_demo
Fix, and fix tests.

Hi,
I am trying to load a CSV with no header using

df = vaex.open('data/star0000-1.csv',sep=",", header=None, error_bad_lines=False)

but I get

could not convert column 0, error: TypeError('getattr(): attribute name must be string'), will try to convert it to string
Giving up column 0, error: TypeError('getattr(): attribute name must be string')
could not convert column 

How to use Watcher / WatcherClient over tcp/ip network?

Watcher seems to ZMQ server, and WatcherClient is ZMQ Client, but there is no API/Interface to config server IP address.
Do I need to implement a class that inherits from WatcherClient?

In the xlearning job web UI, the metrics graph can not be seen for these is no js for it.

Hi, it is a nice work to learning machine learning and I like it very much. I am new to HTML and JS, can you give a step-by-step guide to install, train and play this game?

Currently tslearn/tests/test_estimators.py redefine a custom check_estimator and then monkeypatch some of the tests in sklearn.utils.estimator_checks to work with time series data.

In the latest version scikit-learn introduced parametrize_with_checks which should allow to simplify this quite a bit https://scikit-learn.org/stable/developers/develop.html#rolling-your-own-estimator (e.g. a

Describe the bug

Guide under Tensorflow.js heading needs an update, 'names' flag changed to 'name_policy ' according to latest commit?

Under supported layers heading, ConvTrsnpose2d==ConvTranspose2d?

Enable the ability to ask if we should preload weights/start from original model etc.

This would be much nicer.

Use some good UI stuff like https://github.com/Mckinsey666/bullet

As outlined in #16, it's often useful to extend fine-grained control of sharding to the user. It can be solved by wrapping integers with an identity hash function, but that seems less than ideal. It might be useful to provide this functionality as part of bigslice.Reshuffle.