January 2018
Beginner to intermediate
284 pages
8h 35m
English
Content preview from Deep Learning Essentials
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AlexNet
One of the earliest works in popularizing the use of CNNs in large-scale image classification, AlexNet was proposed by Alex Krizhevsky and their co-authors in 2012. It was submitted as an entry to the ImageNet challenge in 2012 and significantly outperformed its runner-up with a 16% top-5 error rate. AlexNet consists of eight layers in the following order:
[INPUT -> CONV1 -> POOL1 -> CONV2 -> POOL2 -> CONV3 -> CONV5 -> CONV5 -> POOL3 -> FC6 -> FC7 -> FC8].
CONV1 is a convolutional layer with 96 filters of size 11 x 11. CONV2 has 256 filters of size 5 x 5, CONV3 and CONV4 have 384 filters of size 3 x 3, followed by CONV5 with 256 filters of size 3 x 3. All pooling layers, POOL1, POOL2, and POOL3, have 3 x 3 pooling filters. Both FC6 ...
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