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I've been meaning to read Handbook of Neuroevolution Through Erlang. Has anyone done it? If so, what's your opinion about it?

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I'd like to read that book, but the pricing... wow. On Amazon the cheapest new copy is ~ $138.00. And this is one of those cases that shows how goofy pricing for marketplace sellers gets: the cheapest used copy is even worse, at a whopping $230.59. And that's for one in "Good" condition.

Probably all of these resellers are using some brain-dead stupid bot to set their prices for them. Too bad - if somebody was selling a used copy for, say, $75.00, I'd probably order it right now. Instead, it's going to sit in their store forever, because who would pay $230 for a used copy of a book, when they can get a new copy for $138.

SMH.

http://libgen.io/search.php?req=Handbook+of+Neuroevolution+T...

Thanks mate!

Yes, that's a lot, and especially if you don't make six figures today. However, the amount of information it conveys and the potential to round out your skills if this is your area, or if you just like to read and learn like I do, seems fine to me. I don't even work in ML or a related area, and I've spend $1000s on books in this area. I just enjoy studying and practicing it. A friend of mine who criticized the price I spent on another book, spends well over that alone at Starbuck's in 6 weeks! There are PDFs available to see if you think the book is for you before you decide to buy. I like having a hard copy to read when I am not pecking at keys.

Fair enough. I probably would have ordered the $138.00 copy, if I had not just spent a couple of hundred dollars buying other books earlier in the day. :-)

I don't exactly splurge on lots of expensive stuff in my life, but I always figure three thing (in particular) are worth spending on:

1. books

2. food

3. tools

That said, it does seem like book prices have been surging lately. And it is a little frustrating that, more and more, you can't even find a used copy for a reasonable price.

It's very comprehensive, taking the reader from the simplest concepts right up to the state-of-the-art (in 2012). Very friendly, confident and (oddly enough for the subject), exciting! There's a collection of detailed examples, with code. However, unusually for Springer, it clearly wasn't proofread. Lots of repetition and grammatical errors in the text (the code seems solid). It deserves a second edition!

That said, I haven't come across a better book. I'd buy it again without hesitation, regardless of expense. Erlang is way ahead of other popular platforms for neural architectures.

How much of the book requires Erlang or focuses on that language's features, and how much can be generalized to other languages?

The book relies on Erlang's unique features. In Sher's words: "Erlang was created to develop distributed, process based, message passing paradigm oriented, robust, fault tolerant, concurrent systems. All of these features are exactly what a programming language created specifically for developing neural network based systems would have."

If another language can do that (Go, perhaps?), I suppose the code can be made to work adequately.

I have worked through it in Erlang, but others have tried to translate it to LFE (Lisp Flavoured Erlang) and Elixir. It's still relying on the Erlang ecosystem.

Modeling each neuron in an NN as a process is incredibly computationally wasteful, and we often see this in Neuroevolution implementations where neurons are represented as objects. It's clear from advances in DL that neurons and weights should be represented by matrices.

I've never given much credence to Sher's book because of this. Switching to Erlang just isn't necessary given current techniques.

How about using Erlang because it provides fault tolerance, and any faults resulting during the neuroevolutionary process will be contained and won't bring down the whole system? Or evolving groups of neurons in parallel, and across a distributed cluster? These things are much easier in Erlang.

2012 was the year when the modern ML era really began and the explosion of techniques, research, and applications has been tremendous since that year -- sounds like this book ends right when things started getting interesting?

The modern ML era is mired in hype and statistics. Most of the time, all there is to show for it is yet another expert system with baubles.

Neuroevolution is one of those overlooked niches where I think we'll see real progress in real AI.

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