Result for 0B816E1CE0C258DA69F008BEF745AC19B75BFF28

Query result

Key Value
FileName./usr/share/doc/keras-doc/html/callbacks/index.html
FileSize30328
MD5A3B2ECFED3A2F2D6503276ACADDD7845
SHA-10B816E1CE0C258DA69F008BEF745AC19B75BFF28
SHA-256316ED04D5CB4AEA47B65BA2ED43E4A3569E2BEE44183599C6BD0DF54F2275027
SSDEEP768:v+c0yg2tAQtJaNuJStvGLTPluGjP0hrZDb8xrj7xT23N:KygIAQtJaQStvGLTNjjch9Db8RjtT2d
TLSHT188D2A61245EA23374C23B2EF9A2813697A9F904FF65829D1B4EC936CBF41E074A3754D
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