Neural Networks for Control brings together examples of all the most important paradigms for the application of neural networks to robotics and control. Primarily concerned with engineering problems and approaches to their solution through neurocomputing systems, the book is divided into three sections: general principles, motion control, and applications domains (with evaluations of the possible applications by experts in the applications areas.) Special emphasis is placed on designs based on optimization or reinforcement, which will become increasingly important as researchers address more complex engineering challenges or real biological-control problems.A Bradford Book. Neural Network Modeling and Connectionism series

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.
With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.
You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

Text Classification, or the task of automatically assigning semantic categories to natural language text, has become one of the key methods for organizing online information. Since hand-coding classification rules is costly or even impractical, most modern approaches employ machine learning techniques to automatically learn text classifiers from examples. However, none of these conventional approaches combines good prediction performance, theoretical understanding, and efficient training algorithms. Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning. Learning To Classify Text Using Support Vector Machines is designed as a reference for researchers and practitioners, and is suitable as a secondary text for graduate-level students in Computer Science within Machine Learning and Language Technology.

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics.

Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science.
The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation.

Scientists' understanding of two central problems in neuroscience, psychology, and philosophy has been greatly influenced by the work of David Hubel and Torsten Wiesel: (1) What is it to see? This relates to the machinery that underlies visual perception. (2) How do we acquire the brain's mechanisms for vision? This is the nature-nurture question as to whether the nerve connections responsible for vision are innate or whether they develop through experience in the early life of an animal or human. This is a book about the collaboration between Hubel and Wiesel, which began in 1958, lasted until about 1982, and led to a Nobel Prize in 1981. It opens with short autobiographies of both men, describes the state of the field when they started, and tells about the beginnings of their collaboration. It emphasizes the importance of various mentors in their lives, especially Stephen W. Kuffler, who opened up the field by studying the cat retina in 1950, and founded the department of neurobiology at Harvard Medical School, where most of their work was done. The main part of the book consists of Hubel and Wiesel's most important publications. Each reprinted paper is preceded by a foreword that tells how they went about the research, what the difficulties and the pleasures were, and whether they felt a paper was important and why. Each is also followed by an afterword describing how the paper was received and what developments have occurred since its publication. The reader learns things that are often absent from typical scientific publications, including whether the work was difficult, fun, personally rewarding, exhilarating, or just plain tedious. The book ends with a summing-up of the authors' view of the present state of the field. This is much more than a collection of reprinted papers. Above all it tells the story of an unusual scientific collaboration that was hugely enjoyable and served to transform an entire branch of neurobiology. It will appeal to neuroscientists, vision scientists, biologists, psychologists, physicists, historians of science, and to their students and trainees, at all levels from high school on, as well as anyone else who is interested in the scientific process.

In The Birth of the Mind , award-winning cognitive scientist Gary Marcus irrevocably alters the nature vs. nurture debate by linking the findings of the Human Genome Project to the development of the brain. Scientists have long struggled to understand how a tiny number of genes could contain the instructions for building the human brain, arguably the most complex device in the known universe. Synthesizing up-to-the-minute research with his own original findings on child development, Marcus is the first to resolve this apparent contradiction. Vibrantly written and completely accessible to the lay reader, The Birth of the Mind will forever change the way we think about our origins and ourselves.

A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.

This book presents a coherent approach to the fast moving field of machine vision, using a consistent notation based on a detailed understanding of the image formation process. It covers even the most recent research and will provide a useful and current reference for professionals working in the fields of machine vision, image processing, and pattern recognition.An outgrowth of the author's course at MIT, Robot Vision presents a solid framework for understanding existing work and planning future research. Its coverage includes a great deal of material that important to engineers applying machine vision methods in the real world. The chapters on binary image processing, for example, help explain and suggest how to improve the many commercial devices now available. And the material on photometric stereo and the extended Gaussian image points the way to what may be the next thrust in commercialization of the results in this area. The many exercises complement and extend the material in the text, and an extensive bibliography will serve as a useful guide to current research.Contents: Image Formation and Image Sensing. Binary Images: Geometrical Properties; Topological Properties. Regions and Image Segmentation. Image Processing: Continuous Images; Discrete Images. Edges and Edge Finding. Lightness and Color. Reflectance Map: Photometric Stereo Reflectance Map; Shape from Shading. Motion Field and Optical Flow. Photogrammetry and Stereo. Pattern Classification. Polyhedral Objects. Extended Gaussian Images. Passive Navigation and Structure from Motion. Picking Parts out of a Bin.Berthold Klaus Paul Horn is Associate Professor, Department of Electrical Engineering and Computer Science, MIT. Robot Vision is included in the MIT Electrical Engineering and Computer Science Series.

Are we “noble in reason”? Perfect, in God’s image? Far from it, says New York University psychologist Gary Marcus. In this lucid and revealing book, Marcus argues that the mind is not an elegantly designed organ but rather a “kluge,” a clumsy, cobbled-together contraption. He unveils a fundamentally new way of looking at the human mind -- think duct tape, not supercomputer -- that sheds light on some of the most mysterious aspects of human nature.
Taking us on a tour of the fundamental areas of human experience -- memory, belief, decision-making, language, and happiness -- Marcus reveals the myriad ways our minds fall short. He examines why people often vote against their own interests, why money can’t buy happiness, why leaders often stick to bad decisions, and why a sentence like “people people left left” ties us in knots even though it’s only four words long.
Marcus also offers surprisingly effective ways to outwit our inner kluge, for the betterment of ourselves and society. Throughout, he shows how only evolution -- haphazard and undirected -- could have produced the minds we humans have, while making a brilliant case for the power and usefulness of imperfection.