In Keras documentation, glorot_uniform says that the initializer is using Glorot Uniform from this paper. However, the Keras implementation is totally different from the equation on the paper. Also, there are some arguments such as mode ='fan_avg' is the default. It should be same as the referenced paper. 'fan_sum'. Golort uniform is shown

Not sure when this happened but i love the new left-hand side navigation https://scikit-learn.org/dev/user_guide.html

(@adrinjalali did this maybe?)

However, when clicking the different entries, the result is inconsistent. For some, it shows a TOC that expands the existing toc with m

I just started going through the jupyter notebooks (great stuff, by the way), and when running the cells the first time I would always get some error like Polygon has no attribute normed. It turns out that the normed option in plt.hist has been deprecated (in matplotlib version 3.1.1 and later as far as I can tell), and should instead be replaced with density (they appear to operate simi

Alexnet implementation in tensorflow has incomplete architecture where 2 convolution neural layers are missing. This issue is in reference to the python notebook mentioned below.

https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb

I was going though the existing enhancement issues again and though it'd be nice to collect ideas for spaCy plugins and related projects. There are always people in the community who are looking for new things to build, so here's some inspiration ✨ For existing plugins and projects, check out the spaCy universe.

If you have questions about the projects I suggested,

Note 1: This is not the same tab-completion bug for which many issues have already been opened (the one where a massive number of objects from the global namespace are displayed). This issue specifically has to do with unwanted abbreviation of filename paths. I haven't yet found any other reports of this issue.

Note 2: I also posted this to stackoverflow but am posting again here since I didn't

On the chapter about Editable DataTable
we can find this :

This chapter includes recipes for:
    Reading the contents of the DataTable
    Filtering out null values
    Uploading data
    Determining which cell has changed 
    Adding or removing columns
    Adding or removing rows
    Clearable, deletable, renamable, and hideable colum

  /Users/travis/build/ray-project/ray/python/ray/node.py:533: DeprecationWarning: Redis.hmset() is deprecated. Use Redis.hset() instead.
    redis_client.hmset("webui", {"url": self._webui_url})
  /Users/travis/build/ray-project/ray/python/ray/worker.py:358: DeprecationWarning: Redis.hmset() is deprecated. Use Redis.hset() instead.
    "run_on_other_drivers": str(run_on_other_drivers),
  

Example (from TfidfTransformer)

if isinstance(docs[0], tuple):
    docs = [docs]
return [self.gensim_model[doc] for doc in docs]

This method expects a list of tuples, instead of an iterable. This means that the entire corpus has to be stored as a lis

load_csv line can be since there's columns_to_ignore param's supported

data, labels = load_csv('titanic_dataset.csv', target_column=0, columns_to_ignore=[2, 7], categorical_labels=True, n_classes=2)

and we don't need to do that in preprocess()

def preprocess(passengers):
    for i in range(len(passengers)):
        passengers[i][1] = 1. if passengers[i][1] == 'female' else 0.

See https://discuss.streamlit.io/t/support-for-vega-charts/671/2

It would be nice to separately log model computed loss from regularization loss in tensorboard. Involves minor changes to the Trainer.

Description

Add Azure notebook to our SETUP doc.
I tested google colab and Azure notebook to run reco-repo without requiring creating any DSVM or compute by myself, and it works really well with simple tweaks to the notebooks (e.g. for some libs, should install manually).

I think it would be good to add at least Azure notebook to our SETUP doc, where users can easily test out our repo w/o

Describe the bug

The input field to create a project from a URL does not trim its input when validating it.

To Reproduce
Steps to reproduce the behavior:

1. Go to 'Create Project' -> 'Web Addresses (URL)'
2. Paste " http://api.worldbank.org/countries/all/indicators/SP.POP.TOTL?date=2000:2001" with the leading whitespac

As you can see in this example from the documentation docs, there is a linestyles argument, but it doesn't show in the legend. This kind of defies the purpose, printing black/white you still can't tell which is which (except from the markers, but often the markers are hard to read because the line is too wide relativel

Hi,
I'm new to tpot but I got this error. I understand that score function can take strings, but I got the following error when using TPOTClassifier.

---

ValueError Traceback (most recent call last)
in
----> 1 tpot.score(X_test, y_test)

~/miniconda3/envs/ml

Let's enable loading weights from a URL directly

Option 1:

Automate it with our current API

Trainer.load_from_checkpoint('http://')

Option 2:

Have a separate method

Trainer.load_from_checkpoint_at_url('http://')

Resources

We can use this under the hood:
(https://pytorch.org/docs/stable/hub.html#torch.hub.load_state_dict_from_url)

Any tho

(p380) In last sentence of 1st paragraph and first sentence of 2nd paragraph, "Pitt" should be "Pitts".

(p384) In a picture, "3nd Layer" should be "3rd Layer".

(p385) In 1st paragraph, "the o superscript" should be "the out superscript".

(p406) In 1st code block, "print('Training accuracy: ..)" should be "print('Test accuracy: ..)"

(p410) In 1st paragraph,