
The Neural Network Zoo - ZeljkoS
http://www.asimovinstitute.org/neural-network-zoo/
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jbochi
This is already one year old. I am wonder how much the zoo grew in 2017. What
were the main network innovations? Capsules and the Transformer?

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andreyk
DenseNet is a pretty big deal, and I am surprised some version of FCN/U-Net is
not there, or siamese networks are not there. Capsules are still quite young
and Transformer pretty specific, don't know that they have have a large impact
yet.

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anewhnaccount2
The SVM one is a little odd I think? Should all linear leaners (including
naive Bayes, logistic regression, perceptron and SVM) - not share the same
architecture in terms of nodes? As far as I understand it, the difference is
rather in how the discriminator is learnt?

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tomxor
This is excellent.

I love how they have clearly illustrated how the actual details of nodes
themselves can differ greatly between "network types".

As a non-expert I have found this distinction is usually more buried in
general descriptions, which tend to initially focus on topology and behaviour.
I prefer to know those details upfront so I can then map the behaviour as an
emergence as I learn about the behaviour, otherwise when reading about the
behaviour it sounds more like magic.

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debacle
This is fantastic. It'd be nice if this was developed into a full-fledged
wiki.

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mlevental
super useful given that the hard part now is architecture design

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stealthcat
No. The hard part is how to deploy without cloud and without GPU.

Many papers show how to quantize weights from float to binary for super
efficient and faster inference, but nobody did open source their deployment
ready ARM CPU kernels and FPGA HDL code on how to do so.

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svantana
What? Surely deployment is by far the easiest part of machine learning? The
learning is the hard part. If you are resource constrained, then learning a
small/efficient model is even harder, obviously. As for binary math, usually
all you need is the standard bitwise operators and popcount.

