Author : Robin Milner
Publisher : MIT Press
Page : 132 pages
File Size : 47,69 MB
Release : 1997
Category : Computers
ISBN : 9780262631815
Software -- Programming Languages.
Author : Emden R. Gansner
Publisher : Cambridge University Press
Page : 486 pages
File Size : 41,10 MB
Release : 2004-04-05
Category : Computers
ISBN : 9781139451406
The 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
Page : 390 pages
File Size : 22,30 MB
Release : 1999
Category : Computer programming
ISBN :
Based 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 : Åke Wikström
Publisher :
Page : 472 pages
File Size : 18,37 MB
Release : 1987
Category : Computers
ISBN :
Author : Kent D. Lee
Publisher : Springer
Page : 365 pages
File Size : 30,76 MB
Release : 2015-01-19
Category : Computers
ISBN : 3319133144
This 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 : Christoph Molnar
Publisher : Lulu.com
Page : 320 pages
File Size : 46,90 MB
Release : 2020
Category : Computers
ISBN : 0244768528
This 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 : Jon Jagger
Publisher : Morgan Kaufmann
Page : 858 pages
File Size : 42,72 MB
Release : 2010-07-27
Category : Computers
ISBN : 0080550533
Standards, while being definitive, do not usually serve as the best reference to the use of a programming language. Books on languages usually are able to explain usage better, but lack the definitive precision of a standard. Annotated C# Standard combines the two; it is the standard with added explanatory material. Written by members of the standards committee Annotates the standard with practical implementation advice The definitive reference to the C# International Standard
Author : Greg Michaelson
Publisher : Courier Corporation
Page : 338 pages
File Size : 38,58 MB
Release : 2013-04-10
Category : Mathematics
ISBN : 0486280292
Well-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 : G Michaelson
Publisher : CRC Press
Page : 310 pages
File Size : 33,71 MB
Release : 2004-01-10
Category : Computers
ISBN : 0203116593
Standard ML has become the principal teaching language for introducing functional programming. This textbook places emphasis on teaching the essential features of ML, with extensive practical examples, and is intended for undergraduates studying functional programming with Standard ML.
Author : Marc Peter Deisenroth
Publisher : Cambridge University Press
Page : 392 pages
File Size : 28,45 MB
Release : 2020-04-23
Category : Computers
ISBN : 1108569323
The 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.
