Result for 18CD6AE362D07C82B0BE7BDFF309A6118CD7208E

Query result

Key Value
FileName./usr/share/doc/keras-doc/html/getting-started/functional-api-guide/index.html
FileSize29706
MD560310790FE62CCC0FFF4210CB5EA0E26
SHA-118CD6AE362D07C82B0BE7BDFF309A6118CD7208E
SHA-256540965F88DA2B786151C71DA476DA71B43F5265BB193885B60D6D497B1D5DC6A
SSDEEP384:zSRVThaod+xD8r3NUI73IphFITkb6pUgFEhsVJBfIPGlNha3hzhj0IEI+FoIZKtp:u13NXUpsTbVE+lczh7HM2mDlEX3ln
TLSHT10FD2B61225E53736420711F2B78B2B99BFAB0047E6152C90F87C1A1CAF45F166B3B9DE
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