[erlang-questions] eBook version of the Handbook of Neuroevolution Through Erlang, is now available from Springer.
Sun Nov 11 06:02:51 CET 2012
The eBook version of my the Handbook of Neuroevolution Through Erlang, is
now in print: http://www.springer.com/computer/swe/book/978-1-4614-4462-6
The Hardcover book will be available within the next 2-3 weeks from Amazon,
Barnes & Noble, and Springer directly.
- Provides a friendly step-by-step guide on the construction of Topology
and Weight Evolving Artificial Neural Network systems from start to finish
- Covers novel material for using Erlang in the construction of TWEANN
- Explains why Neural Network based Computational Intelligence systems
map perfectly to Erlang’s architecture, and the importance of this
programming language to the future of computational intelligence
- Introduces new TWEANN algorithms, with the final result being a
concurrent, cutting edge, direct and indirect encoded, plasticity enabled,
*Handbook of Neuroevolution Through Erlang* presents both the theory
behind, and the methodology of, developing a neuroevolutionary-based
computational intelligence system using Erlang. With a foreword written by
Joe Armstrong, this handbook offers an extensive tutorial for creating a
state of the art Topology and Weight Evolving Artificial Neural Network
(TWEANN) platform. In a step-by-step format, the reader is guided from a
single simulated neuron to a complete system. By following these steps, the
reader will be able to use novel technology to build a TWEANN system, which
can be applied to Artificial Life simulation, and Forex trading. Because of
Erlang’s architecture, it perfectly matches that of evolutionary and
neurocomptational systems. As a programming language, it is a concurrent,
message passing paradigm which allows the developers to make full use of
the multi-core & multi-cpu systems. *Handbook of Neuroevolution Through
Erlang* explains how to leverage Erlang’s features in the field of machine
learning, and the system’s real world applications, ranging from
algorithmic financial trading to artificial life and robotics.
It covers in detail the subject of Neuroevolution, its applications, why
Erlang is the quintessential neural network programming language, and the
construction of DXNN2: https://github.com/CorticalComputer/DXNN2
A robust, purely Erlang, Topology and Weight Evolving Artificial Neural
Network platform, capable of evolving direct and indirect systems, with and
without plasticity, and a hierarchical structure that yields easily to
allow one to develop self-repairing intelligent agents.
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