Find solutions to problems and answers to questions you are likely to encounter when writing real-world applications in Common Lisp. This book covers areas as diverse as web programming, databases, graphical user interfaces, integration with other programming languages, multi-threading, and mobile devices as well as debugging techniques and optimization, to name just a few. Written by an author who has used Common Lisp in many successful commercial projects over more than a decade, Common Lisp Recipes is also the first Common Lisp book to tackle such advanced topics as environment access, logical pathnames, Gray streams, delivery of executables, pretty printing, setf expansions, or changing the syntax of Common Lisp. The book is organized around specific problems or questions each followed by ready-to-use example solutions and clear explanations of the concepts involved, plus pointers to alternatives and more information. Each recipe can be read independently of the others and thus the book will earn a special place on your bookshelf as a reference work you always want to have within reach. Common Lisp Recipes is aimed at programmers who are already familiar with Common Lisp to a certain extent but do not yet have the experience you typically only get from years of hacking in a specific computer language. It is written in a style that mixes hands-on no-frills pragmatism with precise information and prudent mentorship. If you feel attracted to Common Lisp's mix of breathtaking features and down-to-earth utilitarianism, you'll also like this book.
Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. What you'll learn: How to write code for machine learning, adaptive control and estimation using MATLAB How these three areas complement each other How these three areas are needed for robust machine learning applications How to use MATLAB graphics and visualization tools for machine learning How to code real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB.
Author: Glenn A. Kramer
Publisher: MIT Press
Release Date: 1992
Solving Geometric Constraints records and explains the formal basis for graphical analysis techniques that have been used for decades in engineering disciplines. It describes a novel computer implementation of a 3D graphical analysis method - degrees of freedom analysis - for solving geometric constraint problems of the type encountered in the kinematic analysis of mechanical linkages, providing the best computational bounds yet achieved for this class of problems. The technique allows for the design of algorithms that provide significant speed increases and. will foster the development of interactive software tools for the simulation, optimization, and design of complex mechanical devices as well as provide leverage in other geometric domains. Kramer formalizes symbolic geometry, including explicit reasoning about degrees of freedom, as an alternative to symbolic algebraic or iterative numerical techniques for solving geometric constraint satisfaction problems. He discusses both the theoretical and practical advantages of degrees of freedom analysis, including a correctness proof of the procedure, and clearly defines its scope. He covers all nondegenerate cases and handles several classes of degeneracy, giving examples that are practical and of representative complexity.
Author: F. F. Mazda
Release Date: 2013-10-22
Genre: Technology & Engineering
Electronics Engineer's Reference Book, Sixth Edition is a five-part book that begins with a synopsis of mathematical and electrical techniques used in the analysis of electronic systems. Part II covers physical phenomena, such as electricity, light, and radiation, often met with in electronic systems. Part III contains chapters on basic electronic components and materials, the building blocks of any electronic design. Part IV highlights electronic circuit design and instrumentation. The last part shows the application areas of electronics such as radar and computers.
Author: IEEE Computer Society
Publisher: IEEE Computer Society
Release Date: 1996
Presents papers from the September 1996 conference discussing the application of automated reasoning, knowledge representation, and other artificial intelligence techniques to software engineering problems, with emphasis on constructing and working with software artifacts and processes using knowledge-based techniques. Coverage includes synthesis, verification and validation, knowledge-based environments, and reverse engineering, with papers on areas including applying plan recognition algorithms to program understanding, and synthesis of local search algorithms by algebraic means. No index. Annotation copyrighted by Book News, Inc., Portland, OR.