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A residual block in a deep residual network. Here, the residual connection skips two layers. A residual neural network (also referred to as a residual network or ResNet) [1] is a deep learning architecture in which the layers learn residual functions with reference to the layer inputs.
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 ...
Chollet is the creator of the Keras deep-learning library, released in 2015. His research focuses on computer vision, the application of machine learning to formal reasoning, abstraction, [2] and how to achieve greater generality in artificial intelligence. [3]
[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 can be used in a wide variety of programming languages, including Python, JavaScript, C++, and Java, [12] facilitating its use in a range of applications in many sectors.