Hi, my name is Rachin Kalakheti and i am a participant of Google Code-in 2019. I felt overwhelmed to know Tensorflow is also one of the organization for this year. So, there was a task to create a notebook tutorial on Data Augmentation using tf.image. I see that currently there is no tutorial regarding the same topic. So, I would like to contribute to the community by adding my tutorial to the Te

In the given documentation, the mentioned key are acc and val_acc, but actually it is accuracy and val_accuracy.

Given documentation screenshot:

Whereas the actual keys are `dict_keys(['val_loss', 'val_accuracy

Description

if MultinomialNB there is strange behavior of clf.coef_:
clf.coef_ is the same as clf.feature_log_prob_[1]

and

clf.intercept_ is the same as only one clf.class_log_prior_

for example
clf.feature_log_prob_[0][0:3]

array([-3.63942161, -3.17296199, -4.59417863])

clf.feature_log_prob_[1][0:3]

array([-3.51935008, -3.010937 , -6.41836494])

clf.coef_[0][0:3]

in the rcnn model

`embedded_words_squeezed2.reverse()
embedding_afterward=self.right_side_last_word #tf.zeros((self.batch_size,self.embed_size)) # TODO self.right_side_last_word SHOULD WE ASSIGN A VARIABLE HERE
context_right_afterward = tf.zeros((self.batch_size, self.embed_size)) #self.right_side_context_last # TODO SHOULD WE ASSIGN A VARIABLE HERE
context_right_list

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 benchmark functions provided by tesseract are quite useful however, they aren't exposed by the baseapi.h.

This is a feature request to expose the word error rate computation function tesseract::LSTMTrainer::ComputeWordError(STRING, STRING) through the public facing tesseract/baseapi.h header.

Thank you.

• face_recognition version: 1.2.3
• Python version: 3.7
• Operating System: Debian 10.1

Description

face_detection need to scan "known_people" directory every time.
in "known_people" directory I've 20 people and face_detection need a lot of time to "learn" before search known peoples inside new photos (unknown_pictures directory contain 2 photos).
it's possible to cache "learn" anali

Line 1137 of the Caffe.Proto states "By default, SliceLayer concatenates blobs along the "channels" axis (1)."

Yet, the documentation on http://caffe.berkeleyvision.org/tutorial/layers/slice.html states, "The Slice layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices." which seems to be

As reported on discourse, the REPL help doesn't show tab completion info for emoji even when it exists, unlike for other Unicode completions:

help?> α
"α" can be typed by \alpha<tab>
search:

Couldn't find α
Perhaps you meant !, %, &, *, +, -, /, :, <, >, \, ^, |, ~, 

This should really help to keep a track of papers read so far. I would love to fork the repo and keep on checking the boxes in my local fork.

For example: Have a look at this section. People fork this repo and check the boxes as they finish reading each section.

Considering the MNIST dataset, wich has 5923 instances of the 0 class in the training set, I'm alittle confused about the following code for detemining the relative errors of the SGD classification model:

row_sums = conf_mx.sum(axis=1, keepdims=True)
norm_conf_mx = conf_mx / row_sums

(https://github.com/ageron/handson-ml/blob/master/03_classification.ipynb // In: 67)

Since using `axi

https://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn

Doesn't specify if monotone constraints are usable (it seems like they are but i'm not entirely sure since it doesn't explicitly specify.

Thanks in advance

EDIT: neither does it appear here:

https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst

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

What's the ETA for updating the massively outdated documentation?

Please update all documents that are related building CNTK from source with latest CUDA dependencies that are indicated in CNTK.Common.props and CNTK.Cpp.props.
I tried to build from source, but it's a futile effort.

I am having difficulty in running this package as a Webservice. Would appreciate if we could provide any kind of documentation on implementing an API to get the keypoints from an image. Our aim is to able to deploy this API as an Azure Function and also know if it is feasible.

As I'm using anaconda2(python2 default), so when using :"conda env create -f build/environment.yml" need to specify the python version python=3, like :"conda env create python=3 -f build/environment.yml"

I got a conllU file, from my university, where the head column is filled with .
Processing such file with the cli.convert method will result in a int cast error in
https://github.com/explosion/spaCy/blob/master/spacy/cli/converters/conllu2json.py line 73
in the read_conllx method (head = (int(head) - 1) if head != "0" else id).

In the format documentation on https://universaldependencie

Thanks for maintaining this awesome list. I'd like to suggest to add a release when a change is made to the README file. With this, we can watch the project without getting lots of activity emails.