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[5] [6] It is free and open-source software released under the Apache License 2.0. It was developed by the Google Brain team for Google's internal use in research and production. [7] [8] [9] The initial version was released under the Apache License 2.0 in 2015. [1] [10] Google released an updated version, TensorFlow 2.0, in September 2019. [11]
Up until version 2.3, Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML. [7] [8] [9] As of version 2.4, only TensorFlow was supported. Starting with version 3.0 (as well as its preview version, Keras Core), however, Keras has become multi-backend again, supporting TensorFlow, JAX, and ...
Automatic differentiation [2] Has pretrained models Recurrent nets Convolutional nets RBM/DBNs Parallel execution (multi node) Actively developed BigDL: Jason Dai (Intel) 2016 Apache 2.0: Yes Apache Spark Scala Scala, Python No No Yes Yes Yes Yes Caffe: Berkeley Vision and Learning Center 2013 BSD: Yes Linux, macOS, Windows [3] C++: Python ...
(ie. tensorflow started in 2015, then in 2011 its predecessor came up, then in 2017, a second version was launched, then in 2011 another thing happend, then in 2009 another one, OMG, PLEASE: FIX THIS ORDER. — Preceding unsigned comment added by Drout 0 (talk • contribs) 18:38, 18 December 2024 (UTC)
Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. [1] These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. [1]
Deeplearning4j is open-source software released under Apache License 2.0, [6] developed mainly by a machine learning group headquartered in San Francisco. [7] It is supported commercially by the startup Skymind, which bundles DL4J, TensorFlow , Keras and other deep learning libraries in an enterprise distribution called the Skymind Intelligence ...
Differentiable programming has found use in a wide variety of areas, particularly scientific computing and machine learning. [5] One of the early proposals to adopt such a framework in a systematic fashion to improve upon learning algorithms was made by the Advanced Concepts Team at the European Space Agency in early 2016.
PlaidML is a portable tensor compiler.Tensor compilers bridge the gap between the universal mathematical descriptions of deep learning operations, such as convolution, and the platform and chip-specific code needed to perform those operations with good performance.