An Introduction to Neutral Networks Anderson

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps.

This is a general-purpose textbook about computer networking, complete with diagrams and exercises. It is suitable as the primary text for an undergraduate or introductory graduate course in computer networking, or as a supplemental text for a wide variety of network-related courses.

Beginning R: An Introduction to Statistical Programming is a hands-on book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. R is a powerful open-source implementation of the statistical language S, which was developed by AT&T. R has eclipsed S and the commercially-available S-Plus language, and has become the de facto standard for doing, teaching, and learning computational statistics.

Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat.

The Best Machine Learning Techniques for Data Science in Java and Neuroph with Application in Image Recognition

The Best Machine Learning Techniques for Data Science in Java and Neuroph with Application in Image Recognition

The Best Machine Learning Techniques for Data Science in Java and Neuroph with Application in Image Recognition