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System information

• Have I written custom code (as opposed to using a stock
  example script provided in TensorFlow): Yes

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

cc @ezyang @anjali411 @dylanbespalko

https://github.com/aymericdamien/TensorFlow-Examples/blob/9e1bb504f5d0f209d000997ce2ad95bb891798ab/examples/3_NeuralNetworks/bidirectional_rnn.py#L81

I think "outputs [-1]" and "outputs [0]" are equivalent (reversed) in this line of code, but the former (89%) works better than the latter (86%). Why?

I am executing ML-examples ->cmsisnn-cifar10. While converting trained Caffe model into cmsis-nn by below command

python nn_quantizer.py --model models/cifar10_m7_train_test.prototxt --weights models/cifar10_m7_iter_300000.caffemodel.h5 --save models/cifar10_m7.pkl

getting an error message.

Traceback (most recent call last):
File "nn_quantizer.py", line 614, in
my_model.get_gra

I got this error:

Traceback (most recent call last):
File "c:\Users\jshat\Documents\Code\Machine Learning\Deep-Learning-Papers-Reading-Roadmap\download.py", line 88, in
readme_html = mistune.markdown(readme.read())
File "C:\Python37\lib\encodings\cp1252.py", line 23, in decode
return codecs.charmap_decode(input,self.errors,decoding_table)[0]
UnicodeDecodeError: 'charma

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

Issue Summary

Documentation and error messages are misleading when using a release version of Caffe on Ubuntu.

Executed Command (if any)

cmake .. -DBUILD_CAFFE=OFF -DCaffe_INCLUDE_DIRS=/usr/include/caffe -DCaffe_LIBS=/usr/lib/x86_64-linux-gnu/libcaffe.so

OpenPose Output (if any)

-- The C compiler identification is GNU 7.5.0
-- The CXX compiler identificatio

Hi, is there any plan to provide a tutorial of showing an example of employing the Transformer as an alternative of RNN for seq2seq task such as machine translation?

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 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,

And what packages you have to install to use them

Reference from TensorFlow: https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/matrix-band-part

This op is used by the Music Transformer model.