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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.
Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers ...
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. [1] The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, [2] but lacks a context vector or output gate, resulting in fewer parameters than LSTM. [3]
[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]
LeNet-4 was a larger version of LeNet-1 designed to fit the larger MNIST database. It had more feature maps in its convolutional layers, and had an additional layer of hidden units, fully connected to both the last convolutional layer and to the output units. It has 2 convolutions, 2 average poolings, and 2 fully connected layers.
Vision Transformer architecture, showing the encoder-only Transformer blocks inside. The basic architecture, used by the original 2020 paper, [1] is as follows. In summary, it is a BERT-like encoder-only Transformer.
There is an average pooling of stride 2 at the start of each downsampling convolutional layer (they called it rect-2 blur pooling according to the terminology of [21]). This has the effect of blurring images before downsampling, for antialiasing. [22] The final convolutional layer is followed by a multiheaded attention pooling.
The XLNet was an autoregressive Transformer designed as an improvement over BERT, with 340M parameters and trained on 33 billion words.It was released on 19 June, 2019, under the Apache 2.0 license. [1]