English
5 Feb. 2017
ASIN: B01N5XN28X
96 Pages
PDF (conv)

Encode your own Simple Perceptron line by line, explained and analyzed with practicality. Start copying and learn with explanations and guided exercises, allowing you to gain experience to configure your own Perceptron, and shorten your learning curve, accompanying you with comments from my own experience. "1: My First Perceptron" is the first book in the New Tutorial Series: "Programming Artificial Neural Networks Step by Step", a path of learning and experimentation with practical Sense.

Learn Artificial Neural Network Algorithms with this Tutorial Series, encoding your own Perceptrones; With simple, yet complete, functional and proven examples. It also contains explanations of the parts that compose it for your better understanding. And as the Series progresses, each new algorithm will incorporate new elements, examples and exercises increasing the ability to solve more complex problems.

The exercises are oriented to distinguish where you can begin to experiment, and the observations designed to make you realize what is happening, and where you are taking your changes, With which you will accumulate elements so that you get to design and configure your own Perceptron, adapted to the needs of your projects, be it a robot or an application for decision making. In addition you will have elements to understand more easily Perceptrones Algorithms, although the series already includes of the most used.

So I focus on guiding you through experimentation with the fascinating world of Artificial Neural Networks, codifying it by yourself, so that you accelerate your learning, which I intend to facilitate with at least one complete example by Book and problems that can solve it.

The examples are shown both in Pseudocode, to be able to adapt to any language, as in Python language, not only is easy to learn and understand, is also widely used, and for its clarity, helps you better understand the algorithms; On the other hand, a parallel series is being developed in C language, which is widely used in electronics and PC projects, generates efficient executables, which in large-scale ANN, or resource-reduced projects, is important; so if your language is C, you'd better follow the other Series.

With the Series you can learn to:

Evaluate the needs and resources of your Project

Select which type of ANN to use in your project

Design your own ANN topology

Parametrize the topology and facilitate its improvement dynamically, with less recoding

Configure your ANN according to your needs.

Distinguish which are the parts that consume more computing time, to produce a topology and final code of efficient performance, with controlled memory consumption.

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