ML for the Working Programmer

ML for the Working Programmer
Author: Lawrence C. Paulson
Publsiher: Cambridge University Press
Total Pages: 500
Release: 1996-06-28
Genre: Computers
ISBN: 052156543X

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Software -- Programming Languages.

ML for the Working Programmer

ML for the Working Programmer
Author: Lawrence C. Paulson
Publsiher: Unknown
Total Pages: 429
Release: 1992
Genre: Computers
ISBN: 0521422256

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This new edition of a successful text treats modules in more depth, and covers the revision of ML language.

ML for the Working Programmer

ML for the Working Programmer
Author: Larry C. Paulson
Publsiher: Cambridge University Press
Total Pages: 135
Release: 1996-06-28
Genre: Computers
ISBN: 9781107268494

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The new edition of this successful and established textbook retains its two original intentions of explaining how to program in the ML language, and teaching the fundamentals of functional programming. The major change is the early and prominent coverage of modules, which are extensively used throughout. In addition, the first chapter has been totally rewritten to make the book more accessible to those without experience of programming languages. The main features of new Standard Library for the revised version of ML are described and many new examples are given, while references have also been updated. Dr Paulson has extensive practical experience of ML and has stressed its use as a tool for software engineering; the book contains many useful pieces of code, which are freely available (via the Internet) from the author. He shows how to use lists, trees, higher-order functions and infinite data structures. Many illustrative and practical examples are included.. Efficient functional implementations of arrays, queues, priority queues, etc. are described. Larger examples include a general top-down parser, a lambda-calculus reducer and a theorem prover. The combination of careful explanation and practical advice will ensure that this textbook continues to be the preferred text for many courses on ML.

Elements of ML Programming

Elements of ML Programming
Author: Jeffrey D. Ullman
Publsiher: Pearson
Total Pages: 383
Release: 1998-01
Genre: Computers
ISBN: 0137903871

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This highly accessible introduction to the fundamentals of ML is presented by computer science educator and author, Jeffrey D. Ullman. The primary change in the Second Edition is that it has been thoroughly revised and reorganized to conform to the new language standard called ML97. This is the first book that offers both an accurate step-by-step tutorial to ML programming and a comprehensive reference to advanced features. It is the only book that focuses on the popular SML/NJ implementation. The material is arranged for use in sophomore through graduate level classes or for self-study. This text assumes no previous knowledge of ML or functional programming, and can be used to teach ML as a first programming language. It is also an excellent supplement or reference for programming language concepts, functional programming, or compiler courses.

Introduction to Programming Using SML

Introduction to Programming Using SML
Author: Michael R. Hansen,Hans Rischel
Publsiher: Addison-Wesley
Total Pages: 390
Release: 1999
Genre: Computer programming
ISBN: UCSD:31822027764224

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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

Programming Machine Learning

Programming Machine Learning
Author: Paolo Perrotta
Publsiher: Pragmatic Bookshelf
Total Pages: 437
Release: 2020-03-31
Genre: Computers
ISBN: 9781680507713

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You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.

AI and Machine Learning for Coders

AI and Machine Learning for Coders
Author: Laurence Moroney
Publsiher: O'Reilly Media
Total Pages: 393
Release: 2020-10-01
Genre: Computers
ISBN: 9781492078166

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If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving

The Definition of Standard ML

The Definition of Standard ML
Author: Robin Milner
Publsiher: MIT Press
Total Pages: 132
Release: 1997
Genre: Computers
ISBN: 0262631814

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Software -- Programming Languages.