Deep learning
Deep learning is an AI function and subset of machine learning, used for processing large amounts of complex data.
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https://github.com/keras-team/keras/blob/master/keras/engine/training.py#L1071-L1077
There are two validation_steps.
Not a bug per se, but may as well be, given the amount of confusion resulting:
No one person seems to know, much less understand, the bewildering proliferation of options afforded by cmake/cmake-gui. Especially advanced options.
One of the single greatest benefits to users of OpenCV could be the ability to build OpenCV according to a coherent plan, and not having to comb through countless goo
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.
Mish is a new novel activation function proposed in this paper.
It has shown promising results so far and has been adopted in several packages including:
- TensorFlow-Addons
- SpaCy (Tok2Vec Layer)
- [Thinc - SpaCy's official NLP based ML
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.
Original line 87:
with open('README.md') as readme:
Corrected version of line 87:
with open('README.md','r',encoding='utf-8') as readme:
Explanation:
Windows uses GBK to decode rather than utf-8 at default setting
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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.
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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.
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What's the ETA for updating the massively outdated documentation?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
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With the latest version of scipy.misc, scipy.misc.toimage is no longer available. To load and save an image as png we now have to use PIL, breaking tensorboard image summary.
Here is how I fixed the bug:
1./ At the end of main.py, log a uint8 image
logger.image_summary(tag, (images * 255).astype(np.uint8), step+1)
2./ In Logger class, package image as bytes with the PIL library (mode="L
Traceback (most recent call last):
File "/home/ubuntu/Real-Time-Voice-Cloning-master/toolbox/init.py", line 59, in
self.ui.browser_load_button.clicked.connect(lambda: self.load_from_browser())
File "/home/ubuntu/Real-Time-Voice-Cloning-master/toolbox/init.py", line 122, in load_from_browser
self.add_real_utterance(wav, name, speaker_name)
File "/home/ubuntu/Real
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transcribe.py has odd directory-scanning behavior which isn't documented
If you point --src to a directory, you get the error:
E Path in --src not existing
Looking at the code logic, the script expects a JSON file with a .catalog file extension. This is (1) not documented, and (2) not a really useful logic. It would be much better to point the script to a dir, and scan f
tf.functionmakes invalid assumptions about arguments that areMappinginstances. In general, there are no requirements forMappinginstances to have constructors that accept[(key, value)]initializers, as assumed here.This leads to cryptic exceptions when used with perfectly valid
Mappings