Haskell is a purely functional language that allows programmers to rapidly develop clear, concise, and correct software. The language has grown in popularity in recent years, both in teaching and in industry. This book is based on the author's experience of teaching Haskell for more than twenty years. All concepts are explained from first principles and no programming experience is required, making this book accessible to a broad spectrum of readers. While Part I focuses on basic concepts, Part II introduces the reader to more advanced topics. This new edition has been extensively updated and expanded to include recent and more advanced features of Haskell, new examples and exercises, selected solutions, and freely downloadable lecture slides and example code. The presentation is clean and simple, while also being fully compliant with the latest version of the language, including recent changes concerning applicative, monadic, foldable, and traversable types. Table of Contents Part I Basic Concepts Chapter 1 Introduction Chapter 2 First steps Chapter 3 Types and classes Chapter 4 Defining functions Chapter 5 List comprehensions Chapter 6 Recursive functions Chapter 7 Higher-order functions Chapter 8 Declaring types and classes Chapter 9 The countdown problem Part II Going Further Chapter 10 Interactive programming Chapter 11 Unbeatable tic-tac-toe Chapter 12 Monads and more Chapter 13 Monadic parsing Chapter 14 Foldables and friends Chapter 15 Lazy evaluation Chapter 16 Reasoning about programs Chapter 17 Calculating compilers Appendix A Selected solutions Appendix B Standard prelude

Title: Machine Learning: An Algorithmic Perspective, 2nd Edition Author: Stephen Marsland Length: 457 pages Edition: 2 Language: English Publisher: Chapman and Hall/CRC Publication Date: 2014-10-08 ISBN-10: 1466583282 ISBN-13: 9781466583283 A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. New to the Second Edition Two new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of content Revision of the support vector machine material, including a simple implementation for experiments New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron Additional discussions of the Kalman and particle filters Improved code, including better use of naming conventions in Python Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website. Table of Contents Chapter 1: Introduction Chapter 2: Preliminaries Chapter 3: Neurons, Neural Networks,and Linear Discriminants Chapter 4: The Multi-layer Perceptron Chapter 5: R

Develop, debug, test, and troubleshoot Java EE 7 applications rapidly with Eclipse About This Book Go beyond simply learning Java EE APIs and explore the complete workflow of developing enterprise Java applications Learn to use the features of Eclipse JEE to simplify Java EE application development Develop and deploy complete applications with JEE Who This Book Is For If you are a Java developer who has little or no experience in JEE application development or you have experience in JEE technology but are looking for tips to simplify and accelerate your development process, then this book is for you. What You Will Learn Set up Eclipse, Tomcat, and Glassfish server for JEE application development Use JSP, Servlet, JSF, and EJBs to create a user interface and write business logic Create JEE database applications using JDBC and JPA Handle asynchronous messages using MDBs for better scalability Deploy and debug JEE applications and create SOAP and REST web services Write unit tests and calculate code coverage Troubleshoot application performance and memory issues In Detail Java EE is a technology for developing enterprise class, scalable applications. With recent changes to Java EE specifications, JEE application development has become a lot simpler. However, recent changes have also added many new specifications, some of which compete with existing JEE specification. Along with JEE specifications and APIs, it is also very important to understand the entire application development process and tools that can help simplify and accelerate JEE application development. This guide provides a complete overview of developing JEE applications using Eclipse. The many features of the Eclipse IDE are explained. These enable the rapid development, debugging, testing, and deployment of JEE applications. You'll explore not just different JEE technologies and how to use them (JSP, JSF, JPA, JDBC, EJB, web services etc.), but also suitable technologies for different scenarios. The b

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