.. _chap_modern_cnn:

Modern Convolutional Neural Networks
====================================


Now that we understand the basics of wiring together convolutional
neural networks, we will take you through a tour of modern deep
learning. In this chapter, each section will correspond to a significant
neural network architecture that was at some point (or currently) the
base model upon which an enormous amount of research and projects were
built. Each of these networks was at briefly a dominant architecture and
many were at one point winners or runners-up in the famous ImageNet
competition, which has served as a barometer of progress on supervised
learning in computer vision since 2010.

These models include AlexNet, the first large-scale network deployed to
beat conventional computer vision methods on a large-scale vision
challenge; the VGG network, which makes use of a number of repeating
blocks of elements; the network in network (NiN) which convolves whole
neural networks patch-wise over inputs; the GoogLeNet, which makes use
of networks with parallel concatenations; residual networks (ResNet),
which are the most popular go-to architecture today, and densely
connected networks (DenseNet), which are expensive to compute but have
set some recent benchmarks.

.. toctree::
    :maxdepth: 2

    alexnet
    vgg
    nin
    googlenet
    batch-norm
    resnet
    densenet