The Definition of Standard ML
Author: Robin Milner
Publisher: MIT Press
Published: 1997
Total Pages: 132
ISBN-13: 9780262631815
DOWNLOAD EBOOKSoftware -- Programming Languages.
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Author: Robin Milner
Publisher: MIT Press
Published: 1997
Total Pages: 132
ISBN-13: 9780262631815
DOWNLOAD EBOOKSoftware -- Programming Languages.
Author: Emden R. Gansner
Publisher: Cambridge University Press
Published: 2004-04-05
Total Pages: 486
ISBN-13: 9781139451406
DOWNLOAD EBOOKThe book provides a description of the Standard ML (SML) Basis Library, the standard library for the SML language. For programmers using SML, it provides a complete description of the modules, types and functions composing the library, which is supported by all conforming implementations of the language. The book serves as a programmer's reference, providing manual pages with concise descriptions. In addition, it presents the principles and rationales used in designing the library, and relates these to idioms and examples for using the library. A particular emphasis of the library is to encourage the use of SML in serious system programming. Major features of the library include I/O, a large collection of primitive types, support for internationalization, and a portable operating system interface. This manual will be an indispensable reference for students, professional programmers, and language designers.
Author: Michael R. Hansen
Publisher: Addison-Wesley
Published: 1999
Total Pages: 390
ISBN-13:
DOWNLOAD EBOOKBased on Hanson and Rischel's introductory programming course in the Informatics Programme at the Technical University of Denmark, Using Standard ML (Meta Language) throughout, they bypass theory and customized or efficient implementations to focus on understanding the process of programming and program design. Annotation copyrighted by Book News, Inc., Portland, OR
Author: Christoph Molnar
Publisher: Lulu.com
Published: 2020
Total Pages: 320
ISBN-13: 0244768528
DOWNLOAD EBOOKThis book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Author: Åke Wikström
Publisher:
Published: 1987
Total Pages: 472
ISBN-13:
DOWNLOAD EBOOKAuthor: Greg Michaelson
Publisher: Courier Corporation
Published: 2013-04-10
Total Pages: 338
ISBN-13: 0486280292
DOWNLOAD EBOOKWell-respected text for computer science students provides an accessible introduction to functional programming. Cogent examples illuminate the central ideas, and numerous exercises offer reinforcement. Includes solutions. 1989 edition.
Author: Kent D. Lee
Publisher: Springer
Published: 2015-01-19
Total Pages: 365
ISBN-13: 3319133144
DOWNLOAD EBOOKThis clearly written textbook introduces the reader to the three styles of programming, examining object-oriented/imperative, functional, and logic programming. The focus of the text moves from highly prescriptive languages to very descriptive languages, demonstrating the many and varied ways in which we can think about programming. Designed for interactive learning both inside and outside of the classroom, each programming paradigm is highlighted through the implementation of a non-trivial programming language, demonstrating when each language may be appropriate for a given problem. Features: includes review questions and solved practice exercises, with supplementary code and support files available from an associated website; provides the foundations for understanding how the syntax of a language is formally defined by a grammar; examines assembly language programming using CoCo; introduces C++, Standard ML, and Prolog; describes the development of a type inference system for the language Small.
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
Published: 2020-04-23
Total Pages: 392
ISBN-13: 1108569323
DOWNLOAD EBOOKThe fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Author: Niall Richard Murphy
Publisher: "O'Reilly Media, Inc."
Published: 2016-03-23
Total Pages: 552
ISBN-13: 1491951176
DOWNLOAD EBOOKThe overwhelming majority of a software system’s lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Google’s Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. You’ll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficient—lessons directly applicable to your organization. This book is divided into four sections: Introduction—Learn what site reliability engineering is and why it differs from conventional IT industry practices Principles—Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE) Practices—Understand the theory and practice of an SRE’s day-to-day work: building and operating large distributed computing systems Management—Explore Google's best practices for training, communication, and meetings that your organization can use
Author: Lawrence C. Paulson
Publisher:
Published: 1992
Total Pages: 429
ISBN-13: 9780521422253
DOWNLOAD EBOOKThis new edition of a successful text treats modules in more depth, and covers the revision of ML language.