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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]
TensorFlow is an open source software library powered by Google Brain that allows anyone to utilize machine learning by providing the tools to train one's own neural network. [2] The tool has been used to develop software using deep learning models that farmers use to reduce the amount of manual labor required to sort their yield, by training ...
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 ...
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 ...
Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. [2] Google began using TPUs internally in 2015, and in 2018 made them available for third-party use, both as part of its cloud infrastructure and by ...
(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)
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.
And software supports multiple APIs: OpenGL ES 3.2, Vulkan 1.0, OpenCL 1.1/1.2, OpenVX1.0, AI interfaces support TensorFlow Lite/AndroidNN API. [ 1 ] RK3399 Linux source code and hardware documents are on GitHub [ 2 ] and Wiki opensource website.