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2021
In this chapter we introduce the convolutional neural network theory including concepts such as convolution operator, kernel, stride, padding and pooling.
Ullo S. L. +7 more
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In this chapter we introduce the convolutional neural network theory including concepts such as convolution operator, kernel, stride, padding and pooling.
Ullo S. L. +7 more
openaire +2 more sources
2018
The previously discussed architecture of ANNs is called FC neural networks (FCNNs). The reason is that each neuron in a layer i is connected to all neurons in layers i-1 and i+1. Each connection between two neurons has two parameters: the weight and the bias. Adding more layers and neurons increases the number of parameters.
Mathew Salvaris +2 more
+4 more sources
The previously discussed architecture of ANNs is called FC neural networks (FCNNs). The reason is that each neuron in a layer i is connected to all neurons in layers i-1 and i+1. Each connection between two neurons has two parameters: the weight and the bias. Adding more layers and neurons increases the number of parameters.
Mathew Salvaris +2 more
+4 more sources
2019
In the last few years, convolutional neural networks (CNNs), along with recurrent neural networks (RNNs), have become a basic building block in constructing complex deep learning solutions for various NLP, speech, and time series tasks. LeCun first introduced certain basic parts of the CNN frameworks as a general NN framework to solve various high ...
Uday Kamath, John Liu, James Whitaker
openaire +1 more source
In the last few years, convolutional neural networks (CNNs), along with recurrent neural networks (RNNs), have become a basic building block in constructing complex deep learning solutions for various NLP, speech, and time series tasks. LeCun first introduced certain basic parts of the CNN frameworks as a general NN framework to solve various high ...
Uday Kamath, John Liu, James Whitaker
openaire +1 more source
2017
Artificial neural networks have flourished in recent years in the processing of unstructured data, especially images, text, audio, and speech. Convolutional neural networks (CNNs) work best for such unstructured data. Whenever there is a topology associated with the data, convolutional neural networks do a good job of extracting the important features ...
openaire +1 more source
Artificial neural networks have flourished in recent years in the processing of unstructured data, especially images, text, audio, and speech. Convolutional neural networks (CNNs) work best for such unstructured data. Whenever there is a topology associated with the data, convolutional neural networks do a good job of extracting the important features ...
openaire +1 more source