The architecture of the convolutional neural network with corresponding kernel size (k), number of feature map (n) and stride (s) indicated for each convolutional layer and number of unit for each dense layer.</p
A key challenge in designing convolutional network models is sizing them appro-priately. Many factor...
The design and adjustment of convolutional neural network architectures is an opaque and mostly tria...
Image shows the deep-learning convolutional neural network architecture used in this study.</p
It consists of three convolutional layers with max pooling applied at each layer, along with two ful...
The structure of the convolutional neural network is displayed. Initially, two convolution and max p...
K denotes the number of filters in the first stage of the convolutional layers.</p
<p>The original image has 512 × 512 pixels and 3 RGB channels. Orange and purple squares represent t...
<p>The architecture consists of one input layer, three convolutional layers, two max-pooling layers,...
In this chapter we introduce the convolutional neural network theory including concepts such as conv...
The architecture has three blocks: a convolutional block (CNN), a recursive block (LSTM), and a fina...
In this book, I perform an experimental review on twelve similar types of Convolutional Neural Netwo...
This thesis deals with convolutional neural networks. It is a kind of deep neural networks that are ...
Architecture of the Convolution Neural Network in the hierarchical classifier.</p
<p>The two left layers are for the convolution process and the two right layers are for linear conne...
<p>CNN-1 is composed of three layers, two convolutional layers and an output layer.</p
A key challenge in designing convolutional network models is sizing them appro-priately. Many factor...
The design and adjustment of convolutional neural network architectures is an opaque and mostly tria...
Image shows the deep-learning convolutional neural network architecture used in this study.</p
It consists of three convolutional layers with max pooling applied at each layer, along with two ful...
The structure of the convolutional neural network is displayed. Initially, two convolution and max p...
K denotes the number of filters in the first stage of the convolutional layers.</p
<p>The original image has 512 × 512 pixels and 3 RGB channels. Orange and purple squares represent t...
<p>The architecture consists of one input layer, three convolutional layers, two max-pooling layers,...
In this chapter we introduce the convolutional neural network theory including concepts such as conv...
The architecture has three blocks: a convolutional block (CNN), a recursive block (LSTM), and a fina...
In this book, I perform an experimental review on twelve similar types of Convolutional Neural Netwo...
This thesis deals with convolutional neural networks. It is a kind of deep neural networks that are ...
Architecture of the Convolution Neural Network in the hierarchical classifier.</p
<p>The two left layers are for the convolution process and the two right layers are for linear conne...
<p>CNN-1 is composed of three layers, two convolutional layers and an output layer.</p
A key challenge in designing convolutional network models is sizing them appro-priately. Many factor...
The design and adjustment of convolutional neural network architectures is an opaque and mostly tria...
Image shows the deep-learning convolutional neural network architecture used in this study.</p