CleverAlgorithms

You might like these- - Matrix Arcade (https://yizhe-ang.github.io/matrix-explorable//) - Computational Discovery on Jupyter (https://computational-discovery-on-jupyter.github.io/Computa...) - Nature Inspired Programming Recipes (https://github.com/clever-algorithms/CleverAlgorithms) - A Little Calculus (https://papl.cs.brown.edu/2018/func-as-data.html#%28part._.A...) - The Matrix in Computer Science (https://cs.brown.edu/courses/cs053/current/lectures.htm) - Discrete Mathematics and Functional Programming (https://cs.wheaton.edu/~tvandrun/dmfp/) - A Programmer's Introduction to Mathematics (https://pimbook.org/) - Complexity Explorer (https://www.complexityexplorer.org/) - Learn Physics with Functional Programming (https://nostarch.com/learn-physics-functional-programming) - Seeing Theory (https://seeing-theory.brown.edu/) - Coding The M

There's a lot of them. Here's one interesting book that catalogs many, with implementations. https://github.com/clever-algorithms/CleverAlgorithms Also, not sure if they're in this book or not, going from memory (I haven't looked at it in a while) but a few other things that might be relevant here, that I haven't seen mentioned yet: https://en.wikipedia.org/wiki/Flocking_(behavior) -- simulations of animal flocking behavior have found some applications https://en.wikipedia.org/wiki/Artificial_life - ALife in general may be thought of as slightly "nature inspired", or at least certain approaches to it https://en.wikipedia.org/wiki/Boids - Boids specifically is an ALife approach that heavily uses Flocking The OP might also find something of interest in "chemical computing" and/or "DNA computing". https://en.wikipedia.org/wiki/Chemical_compu

Maybe Clever Algorithms: Nature-Inspired Programming Recipes[1]. These techniques might not be useful to everybody, all the time, but they can be very handy in places. Edit: A couple of other suggestions. Managing Gigabytes: Compressing and Indexing Documents and Images[2] Mining of Massive Datasets[3] Algorithm Design Manual[4] Network Algorithmics[5] Neural Network Design[6] I think all of these fall into the category of "Won't be applicable to everyone, but can be good for those who need this kind of stuff." [1]: https://github.com/clever-algorithms/CleverAlgorithms [2]: https://people.eng.unimelb.edu.au/ammoffat/mg/ [3]: http://www.mmds.org/ [4]: https://www.amazon.com/Algorithm-Design-Manual-Steven-Skiena... [5]: https://www.amazon.com/Network-Algorithmics-Interdisciplinar... [6]: https://hagan.okstate.edu/NNDesign.pdf

I have a little skype group where we discuss AI. If you are interested you can add me on skype: Switch336 As for learning AI it is best to learn it in the language that you are strongest in or a language that is easy to understand the semantics from it. For that matter Lua, Ruby, and Python are fairly commonplace for people trying to learn how to use AI. But AI algorithms can be written in any language really. I'd suggest books with common examples of AI; like https://github.com/jbrownlee/CleverAlgorithms. He uses ruby for most of their examples but they are fairly understandable. If you cannot get the books you want. You may be better off checking out the source codes at least. There are tons of free online books if you do some more searching. And you can even read some online ipython notebooks or blog posts about AI that may be more hel

Nice catch. I just grepped the text and can see what you mean. I've logged an issue for it: https://github.com/jbrownlee/CleverAlgorithms/issues#issue/1...

I am no longer an academic. I had a go at the startup thing and am now a PhD surviving in industry :) The book has been released (today!) as a paperback, free PDF and free online. You may also fork the content on github if your keen: https://github.com/jbrownlee/CleverAlgorithms I'm happy to answer any questions. Some guff from the back cover of the book: Implementing an Artificial Intelligence algorithm is difficult. Algorithm descriptions may be incomplete, inconsistent, and distributed across a number of papers, chapters and even websites. This can result in varied interpretations of algorithms, undue attrition of algorithms, and ultimately bad science. This book is an effort to address these issues by providing a handbook of algorithmic recipes drawn from the fields of Metaheuristics, Biologically Inspired Computation and Computationa

I just pressed "publish" on this book only hours ago. I'm happy to answer any questions. From the back cover: Implementing an Artificial Intelligence algorithm is difficult. Algorithm descriptions may be incomplete, inconsistent, and distributed across a number of papers, chapters and even websites. This can result in varied interpretations of algorithms, undue attrition of algorithms, and ultimately bad science. This book is an effort to address these issues by providing a handbook of algorithmic recipes drawn from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence, described in a complete, consistent, and centralized manner. These standardized descriptions were carefully designed to be accessible, usable, and understandable. Most of the algorithms described were originally inspired by biologic