This rich, fascinating, accessible hands on guide,
puts neural networks firmly into the hands of the
practitioner. It reveals how they work, and takes
you under the hood with an easy to follow
process showing you how to build them faster
than you imagined possible using the powerful,
free R predictive analytics package.

Everything
you need to get started is contained within this
book. It is your detailed, practical, tactical hands
on guide. To accelerate your success, it contains
exercises with fully worked solutions also
provided. Once you have mastered the process, it
will be easy for you to translate your knowledge
into other powerful applications.


A book for everyone interested in machine
learning, predictive analytics, neural networks and
decision science.

Here is what it can do for you:

SAVE TIME: Imagine having at your fingertips
easy access to the very best neural network
models without getting bogged down in
mathematical details. In this book, you'll learn
fast effective ways to build powerful neural
network models easily using R.

LEARN EASILY: Build Your Own Neural Network
TODAY! Contains an easy to follow process
showing you how to build the most successful
neural networks used for learning from data; use
this guide and build them easily and quickly.

BOOST PRODUCTIVITY: Bestselling author and
data scientist Dr. N.D. Lewis will show you how
to build neural network models in less time than
you ever imagined possible! Even if you're a busy
professional, a student or hobbyist with little
time, you will rapidly enhance your knowledge.

EFFORTLESS SUCCESS: By spending as little as
10 minutes a day working through the dozens of
real world examples, illustrations, practitioner tips
and notes, you'll be able to make giant leaps
forward in your knowledge, broaden your skill-set
and generate new ideas for your own personal
use.

ELIMINATE ANXIETY: Forget trying to master
every single mathematical detail, instead your
goal is to simply to follow the process using real
data that only takes about 5 to 15 minutes to
complete. Within this process is a series of
actions by which the neural network model is
explained and constructed. All you have to do is
follow the process. It is your checklist for use
and reuse.


For people interested in statistics, machine
learning, data analysis, data mining, and future
hands-on practitioners seeking a career in the
field, it sets a strong foundation, delivers the
prerequisite knowledge, and whets your appetite
for more.
Here are some of the neural network models you
will build:

*Multi layer Perceptrons

*Probabilistic Neural Networks

*Generalized Regression Neural Networks

*Recurrent Neural Networks.

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