February 2019
Intermediate to advanced
260 pages
6h 3m
English
Content preview from Hands-On Java Deep Learning for Computer Vision
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VGG-16
The following architecture of VGG-16 was developed in the year 2015 by K. Simonyan and A. Zisserman from the University of Oxford. It not only has more parameters, but it's also more uniform and simpler to reason:
Instead of having different sizes of filters, as AlexNet does, it has the same type of filters, so it has a convolution same, 3 x 3, and it has a max pooling of 2 x 2, with a stride of two. When we talk about convolution, we'll always have a 3 x 3, convolution same, and when we say pool, we'll always have a max pooling of 2 x 2 with a stride of 2, which usually divides the first 2 dimensions by 2, while the same convolutional ...
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