Result for 03D8DAE14581AD389DC18D564D4C0E09D64B5437

Query result

Key Value
FileName./usr/share/doc/keras-doc/html/mkdocs/search_index.json.gz
FileSize109923
MD5450FF80D73077A3718238858988406ED
SHA-103D8DAE14581AD389DC18D564D4C0E09D64B5437
SHA-2564EE83CA5C6D490FB460FB70B7FDA846C8ED6455CC578F96996C60C9542653670
SSDEEP3072:BvT1vIVgfs/Ys4XK0LrJRrWhEnlR15L6THQVoKe0Aw6js3:BvTVIV6s/YzLWhAR15LGQWV0T6o3
TLSHT16EB3129EBB36DCCCD429248FD04DBF542062DDA5404B1AC9FB91E0C9AA3CF61D269B91
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize942032
MD59EE8D4352481D1DAC8F8C26E165B1DA1
PackageDescriptionCPU/GPU math expression compiler for Python (docs) Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation. . Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing). . It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian, but coming up). . This package contains the documentation for Keras.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamekeras-doc
PackageSectiondoc
PackageVersion2.1.1-1
SHA-1D1759E97E0267A861FA9A6EB5568E23251D070B1
SHA-256EF91FD03A896D2F8D5E0C260E64F27DEA9395684BD6FDCAD82E0CB480C84E03E