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R-CNN architecture Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision , and specifically object detection and localization. [ 1 ] The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output, where each bounding box contains an object and also the ...
A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [ 1 ]
Recurrent neural networks (RNNs) are a class of artificial neural networks designed for processing sequential data, such as text, speech, and time series, [1] where the order of elements is important.
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AlexNet block diagram. AlexNet is a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton, who was Krizhevsky's Ph.D. advisor at the University of Toronto in 2012. It had 60 million parameters and 650,000 neurons. [1]
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LeNet-5 architecture block diagram LeNet-5 architecture (detailed). 1998 LeNet is similar to 1994 LeNet, but with more fully connected layers. Its architecture is shown in the image on the right. It has 2 convolutions, 2 average poolings, and 3 fully connected layers. 1998 LeNet was trained for about 20 epoches over MNIST.