Showing posts with label pattern recognition. Show all posts
Showing posts with label pattern recognition. Show all posts

An Introduction to Neural Networks Review

An Introduction to Neural Networks
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An Introduction to Neural Networks ReviewThis is one of the best books I have ever read. It introduces neural networks, with a strong emphasis on biological plausibility. For example, the book compares the visual systems of simple animals with neural network feature extraction. Anderson moves effectively among evolutionary biology, cognitive science, artificial intelligence, and behavioral psychology. His insights are important, clear, and often funny as well. The book gently introduces source code for implementing the various neural networks that he describes.An Introduction to Neural Networks Overview

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Introduction to Autonomous Mobile Robots (Intelligent Robotics and Autonomous Agents series) Review

Introduction to Autonomous Mobile Robots (Intelligent Robotics and Autonomous Agents series)
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Introduction to Autonomous Mobile Robots (Intelligent Robotics and Autonomous Agents series) ReviewThis book is one of only a few of its kind. That is, it is a book that focuses on the computational issues in robotics rather than the all-too familiar hobbyist approach.
Chapter one is somewhat expendable, since it is simply a prosaic overview of the entire text. Chapter two talks about the theory of robot motion at a high level, with few helpful technical details. Chapter three, "Mobile Robot Kinematics", is where the book takes a dramatic upturn in quality. The computations of robot kinematics are discussed in detail for several kinds of robotic wheels. To really understand this chapter, the reader should already be familiar with dynamics and statics as well as the concepts of three dimensional rotation and matrices. For a companion to the kinematic material, I suggest Craig's "Introduction to Robotics: Mechanics and Control (3rd Edition)". It is much better than the reviewers say. Chapter four covers the various sensors used in robotics, as well as the mathematics needed in the integration of those sensors. The chapter concludes with some basic image processing and computer vision techniques. The material here is broad but somewhat shallow. I suggest that you already be familiar with basic image processing techniques and computer vision, or possibly have a copy of Gonzales & Woods "Digital Image Processing" handy, as well as a copy of Trucco & Verri's "Introductory Techniques for 3D Computer Vision". This is especially true if you wish to grasp some of the statistical techniques at the end of chapter four. Chapters five and six, on localization and navigation, respectively, are mathematically deep. The material on the Kalman Filter is somewhat oversimplified, and to truly grasp the subject of Kalman Filtering one will need to go to an outside source for a full tutorial. In my case, my instructor's notes were sufficient.
In summary, this is a very good book for someone who is mathematically mature enough to handle the computational aspects of mobile robotics. The reader should already understand dynamics, statics, numerical linear algebra, image processing, artificial intelligence, and computer vision. This book collects and connects all of these concepts well, but you will have to go digging elsewhere for the complete amount of detail required to perform all of the calculations for the methods described in this book. In short, computational robotics is a type of "capstone course" for engineers/computer scientists with the correct background and reference library, and for those engineers, this is the best guide published to date.Introduction to Autonomous Mobile Robots (Intelligent Robotics and Autonomous Agents series) Overview

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