Neural Network
Artificial neural networks (ANN) are computational systems that "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.
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🐛 Bug
Compiling against the C++ API on macOS using GCC-9.3, and cmake seems to use a bad flag:
... -fopenmp -D_GLIBCXX_USE_CXX11_ABI= -std=c++14 ... -- note how it "blanks out" the _GLIBCXX_USE_CXX11_ABI variable. This causes the compiler to fail in the stdlib:
/usr/local/Cellar/gcc@9/9.3.0/include/c++/9.3.0/x86_64-apple-darwin18/bits/c++config.h:273:27: error: #if with no expr-
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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.
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We plan to gradually migrate brain.js to TypeScript, code base is pretty large, so we would love your help!
How to contribute?
- Convert a file from .js to .ts
- Add types, fix all type errors.
- Submit a PR!
🎉
Here you can find a guide on how to contribute.
Want to convert something, let us know in the comment an
Not a high-priority at all, but it'd be more sensible for such a tutorial/testing utility corpus to be implemented elsewhere - maybe under /test/ or some other data- or doc- related module – rather than in gensim.models.word2vec.
Originally posted by @gojomo in RaRe-Technologies/gensim#2939 (comment)
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Bug Report
These tests were run on s390x. s390x is big-endian architecture.
Failure log for helper_test.py
________________________________________________ TestHelperTensorFunctions.test_make_tensor ________________________________________________
self = <helper_test.TestHelperTensorFunctions testMethod=test_make_tensor>
def test_make_tensor(self): # type: () -> None
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as the benchmark examples
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Hi I would like to propose a better implementation for 'test_indices':
We can remove the unneeded np.array casting:
Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))
Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))
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Sep 23, 2020 - Jupyter Notebook


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