tf.function makes invalid assumptions about arguments that are Mapping instances. In general, there are no requirements for Mapping instances to have constructors that accept [(key, value)] initializers, as assumed here.

This leads to cryptic exceptions when used with perfectly valid Mapping s

Some lines in the code block of the keras docs is too long, the result of which is, there will be a horizonal scroll bar at the bottom of the code block. That is hard to read. The long lines should be rearranged to multiple short lines to improve readibility.

Example:
The docs for the SimpleRNN class (https://keras.io/layers/recurrent/#simplernn). The initializer of SimpleRNN has m

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]

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb

trainable_variables = weights.values() + biases.values() doesn't work.
Also if I write trainable_variables = list(weights.values()) + list(biases.values()), I have to turn on tf.enable_eager_execution(), but the training result is wrong, accuracy is ar

Current implementation does sequential sigmoid_out and mul_. We can get better performance by fusing this operations together.

Current Behavior:

The the wiki page APIExample, for the python example, the handle api is is run through the TessBaseAPIDelete funciton if the api failed to be initialized whereas for the C example below, this is not the case.

python:

rc = tesseract.TessBaseAPIInit3(api, TESSDATA_PREFIX, lang)
if (rc):
    te

• face_recognition version:4.1.0
• Python version:3.7
• Operating System:windows 10

Description

I'm having problem to run this program, the error under below

gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
cv2.error: OpenCV(4.1.0) C:\projects\opencv-python\opencv\modules\imgp

studentc@2080ti:~/caffe$ make all
PROTOC src/caffe/proto/caffe.proto
CXX .build_release/src/caffe/proto/caffe.pb.cc
CXX src/caffe/blob.cpp
CXX src/caffe/syncedmem.cpp
CXX src/caffe/net.cpp
CXX src/caffe/data_transformer.cpp
CXX src/caffe/layer.cpp
CXX src/caffe/common.cpp
CXX src/caffe/parallel.cpp
CXX src/caffe/layers/infogain_loss_layer.cpp
src/caffe/layers/infogain_loss_layer.cpp: In

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.

Currently as follows:

julia> abstract type Foo{S}; end

julia> struct Bar <: Foo; end
ERROR: invalid subtyping in definition of Bar
Stacktrace:
 [1] top-level scope at REPL[2]:1

Ideally it would at least tell you you forgot a type parameter, and maybe if it's extra nice show you the signature of the thing you're trying to subtype to show you what type parameters it has.

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.

I'm not sure if XGBoost s model is well calibrated with softmax. It would be nice to have a doc with various experiments including random forest, dart etc.

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.

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

I recently added "back to top" button to README. What other features would make it easier to browse? Please write your recommendation.

Some courses have login pages so that only students of the institution can view the material. Should these courses be left on the list or should they be taken out seeing that they cannot be accessed by the general public?

According to the List_of_unsolved_problems_in_computer_science

Is there any perfect stemming algorithm in the English language?

I believe that lemmatization is not solved too.

It would be wonderful to add the states of the arts in both tasks.
BTW, lemmatization