Book picks similar to
Adaptive Pattern Recognition and Neural Networks by Yoh-Han Pao
egr229
egr229c-c5
computer-science
deep-learning-neural-networks
The Complete Wizard of Oz Collection: All 22 Stories With Active Table of Contents
L. Frank Baum - 2012
Frank Baum annotated with his detailed biography. This is the only complete collection that includes all the Oz stories. This kindle title comes with an active table of content as well as a table of content for each story. There are also links to the story table of contents and the main table of contents at the start of each chapter to make navigating this huge collection easier. You can also easily navigate from one chapter to another or from one story to another using the menu button.All the 22 stories are fully complete. They even include the dedication and the introduction of each story/book.In this collection, you will find stories not available in other collections and it even include stories published after the death of L. Frank Baum.About The Wizard of Oz:Ever since Dorothy realized that she was not in Kansas anymore, she has encountered nothing but excitement and adventure in the Land of Oz. She befriends talking creatures, magical constructs, and even powerful witches and wizards. Together they overthrow evil and repel invaders, setting things right in the land once more, all while discovering the inner strength and courage that each one of them possess.Dorothy and other visitors always find themselves in the Land of Oz, where some adventure always awaits them. Other heroes hail from the Land of Oz itself, merely trying to survive the perils of living in this magical land. At the end of each adventure, however, the heroes can always look forward to a feast in Emerald City, after which Dorothy herself will always find her way back home to her aunt and uncle.So, grab this title, and follow along the adventures!Included in this collection:(Stories are listed according to publication date)1. The Wonderful Wizard of Oz2. The Marvelous Land of Oz3. The Woggle-Bug Book4. Ozma of Oz5. Dorothy and the Wizard in Oz6. The Road to Oz7. The Emerald City of Oz8. The Patchwork Girl of OzLittle Wizard Stories of Oz:9. The Cowardly Lion and the Hungry Tiger10. Little Dorothy and Toto11. Tiktok and the Nome King12. Ozma and the Little Wizard13. Jack Pumpkinhead and the Sawhorse14. The Scarecrow and the Tin Woodman15. Tik-Tok of Oz16. The Scarecrow of Oz17. Rinkitink in Oz18. The Lost Princess of Oz19. The Tin Woodman of Oz20. The Magic of Oz21. Glinda of Oz22. The Royal Book of Oz(description courtesy Amazon.com: http://www.amazon.com/gp/product/B008... )
Fatal Edge
Diane Capri - 2017
Along with her new P.I. boyfriend, Mandy heads up to the ski resort in Wyoming where the woman was last seen and lands in the path of a killer. Can Jess get there before Mandy becomes the next victim?
The Art and Science of Java
Eric S. Roberts - 2007
By following the recommendations of the Association of Computing Machinery's Java Task Force, this first edition text adopts a modern objects-first approach that introduces readers to useful hierarchies from the very beginning.KEY TOPICS: Introduction; Programming by Example; Expressions; Statement Forms; Methods; Objects and Classes; Objects and Memory; Strings and Characters; Object-Oriented Graphics; Event-Driven Programs; Arrays and ArrayLists; Searching and Sorting; Collection Classes; Looking Ahead.MARKET: A modern objects-first approach to the Java programming language that introduces readers to useful class hierarchies from the very beginning.
Deep Learning
John D. Kelleher - 2019
When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.
Software Tools
Brian W. Kernighan - 1976
The programs contained in the book are not artificial, but are actual programs ae tools which have proved valuable in the production of other programs.Modern programming techniques such as structured programming and top-down design are emphasized and applied to every program. The programs are presented in a structured language called Ratfor ("Rational Fortran") which can be easily understood by anyone familiar with Fortran or PL/I, Algol, PASCAL, or similar languages. (Ratfor translates readily into Fortran or PL/I. One of the tools presented is a preprocessor to translate Ratfor into Fortran). All of the programs are complete and have been tested directly from the text. The programs are available in machine-readable form from Addison-Wesley.Software Tools is ideal for use in a "software engineering" course, for a second course in programming, or as a supplement in any programming course. All programmers, professional and student, will find the book invaluable as a source of proven, useful programs for reading and study. Numerous exercises are provided to test comprehension and to extend the concepts presented in the text.
Bayes Theorem Examples: An Intuitive Guide
Scott Hartshorn - 2016
Essentially, you are estimating a probability, but then updating that estimate based on other things that you know. This book is designed to give you an intuitive understanding of how to use Bayes Theorem. It starts with the definition of what Bayes Theorem is, but the focus of the book is on providing examples that you can follow and duplicate. Most of the examples are calculated in Excel, which is useful for updating probability if you have dozens or hundreds of data points to roll in.
The R Book
Michael J. Crawley - 2007
The R language is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets.
Thinking in CSS
Aravind Shenoy - 2014
Instead of wandering through loads of theory, we will understand CSS more practically so that we can design a webpage using CSS. We have used Notepad for the examples in this book. Alternatively, you can also use Notepad++ or any advanced editor. All that you need to do is copy the code and paste it into Notepad. Upon execution, you will get the output as depicted in the screenshots. Screenshots are provided for each sample code. Coding gets better with practice. The examples in this book are compatible with almost every browser. Instead of using the verbatim code, you can modify the code and see the change in the output, thereby understanding the subtle nuances of CSS. By the end of the book, with practice, you can achieve better things and get more acquainted with CSS.
Machine Learning: The Art and Science of Algorithms That Make Sense of Data
Peter Flach - 2012
Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.
Bandit Algorithms for Website Optimization
John Myles White - 2012
Author John Myles White shows you how this powerful class of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success.This is the first developer-focused book on bandit algorithms, which were previously described only in research papers. You’ll quickly learn the benefits of several simple algorithms—including the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithms—by working through code examples written in Python, which you can easily adapt for deployment on your own website.Learn the basics of A/B testing—and recognize when it’s better to use bandit algorithmsDevelop a unit testing framework for debugging bandit algorithmsGet additional code examples written in Julia, Ruby, and JavaScript with supplemental online materials
Bayesian Reasoning and Machine Learning
David Barber - 2012
They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
Intermediate Perl
Randal L. Schwartz - 2003
One slogan of Perl is that it makes easy things easy and hard things possible. "Intermediate Perl" is about making the leap from the easy things to the hard ones.Originally released in 2003 as "Learning Perl Objects, References, and Modules" and revised and updated for Perl 5.8, this book offers a gentle but thorough introduction to intermediate programming in Perl. Written by the authors of the best-selling "Learning Perl," it picks up where that book left off. Topics include: Packages and namespacesReferences and scopingManipulating complex data structuresObject-oriented programmingWriting and using modulesTesting Perl codeContributing to CPANFollowing the successful format of "Learning Perl," we designed each chapter in the book to be small enough to be read in just an hour or two, ending with a series of exercises to help you practice what you've learned. To use the book, you just need to be familiar with the material in "Learning Perl" and have ambition to go further.Perl is a different language to different people. It is a quick scripting tool for some, and a fully-featured object-oriented language for others. It is used for everything from performing quick global replacements on text files, to crunching huge, complex sets of scientific data that take weeks to process. Perl is what you make of it. But regardless of what you use Perl for, this book helps you do it more effectively, efficiently, and elegantly."Intermediate Perl" is about learning to use Perl as a programming language, and not just a scripting language. This is the book that turns the Perl dabbler into the Perl programmer.
Preacher's Justice / Fury of the Mountain Man (The First Mountain Man, #10 ; Mountain Man, #12)
William W. Johnstone - 2007
Fundamentals of Computer Algorithms
Ellis Horowitz - 1978
The book comprises chapters on elementary data structures, dynamic programming, backtracking, algebraic problems, lower bound theory, pram algorithms, mesh algorithms, and hypercube algorithms. In addition, the book consists of several real-world examples to understand the concepts better. This book is indispensable for computer engineers preparing for competitive examinations like GATE and IES.
30 Arduino Projects for the Evil Genius
Simon Monk - 2010
You'll master Arduino through a series of projects while learning the required simplified C programming.