If I have read your comment correctly, I'll say it for you: as an outsider, I read this carefully until I gave up because it was too technical, which happened right at the very top, in the third paragraph:
>Those hidden layers normally have some sort of sigmoid activation function (log-sigmoid or the hyperbolic tangent etc.). For example, think of a log-sigmoid unit in our network as a logistic regression unit that returns continuous values outputs in the range 0-1
All this implies I know all about multi-layer perceptrons - and I don't. I can't follow the instructions to "think of a log-sigmoid unit in our network as a logistic regression unit" because I don't know what those terms mean.
Just as I would give up on a recipe if I got to an instruction I didn't know. For example, if I read:
>Glaze the meringue with a blow torch, or briefly in a hot oven.
Yeah, uh, no... I don't even know what glazing means, or what is "briefly in a hot oven". So I just stop reading. When I'm instructed to do something I can't, I go look at something else unless I'm feeling very adventurous.[1]
This blog post isn't written at my level.
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[1] as a last hoorah I'll open a tab and Google https://www.google.com/search?q=what+is+glazing - likewise I tried https://www.google.com/search?q=what+is+a+multilayer+percept... but decided after reading the Wikipedia link that it was too "deep" for me.