<p>They are the same type of Networks. Convolutional Neural Networks. The problem with the overview is that as soon as you post something it is already outdated. Most of the networks you describe are already old, even though they are only a few years old.</p>

<p>Nevertheless you can take a look at the networks supplied by caffe (<a href="https://github.com/BVLC/caffe/tree/master/models" rel="nofollow noreferrer">https://github.com/BVLC/caffe/tree/master/models</a>). </p>

<p>In my personal view the most important concepts in deep Learning are recurrent networks (<a href="https://keras.io/layers/recurrent/" rel="nofollow noreferrer">https://keras.io/layers/recurrent/</a>), residual connections, inception blocks (see <a href="https://arxiv.org/abs/1602.07261" rel="nofollow noreferrer">https://arxiv.org/abs/1602.07261</a>). The rest are largely theoretical concepts, which would not fit in a stack overflow answer.</p>
Overview for Deep Learning Networks
<p>I am fairly new to Deep Learning and get quite overwhelmed by the many different Nets and their field of application. Thus, I want to know if there is some kind of overview which kind of different networks exist, what there key-features are and what kind of purpose they have.</p>

<p>For example I know abut LeNet, ConvNet, AlexNet - and somehow they are the same but still differ? </p>