Apress | Practical Python AI Projects: Mathematical Models Of Optimization Problems With Google OR-Tools (2018 EN)

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    Author: Serge Kruk
    Full Title: Practical Python AI Projects: Mathematical Models Of Optimization Problems With Google OR-Tools
    Publisher: Apress; 1st ed. edition (February 27, 2018)
    Year: 2018
    ISBN-13: 9781484234235 (978-1-4842-3423-5), 9781484234228 (978-1-4842-3422-8)
    ISBN-10: 1484234235, 1484234227
    Pages: 279
    Language: English
    Genre: Educational: Artificial Intelligence
    File type: EPUB (True), PDF (True), Code Files
    Quality: 10/10
    Price: 26.74 €


    Discover the art and science of solving artificial intelligence problems with Python using optimization modeling. This book covers the practical creation and analysis of mathematical algebraic models such as linear continuous models, non-obviously linear continuous models,and pure linear integer models. Rather than focus on theory, Practical Python AI Projects, the product of the author's decades of industry teaching and consulting, stresses the model creation aspect; contrasting alternate approaches and practical variations.

    Each model is explained thoroughly and written to be executed. The source code from all examples in the book is available, written in Python using Google OR-Tools. It also includes a random problem generator, useful for industry application or study.


    Learn:
    ✓ Build basic Python-based artificial intelligence (AI) applications
    ✓ Work with mathematical optimization methods and the Google OR-Tools (Optimization Tools) suite
    ✓ Create several types of projects using Python and Google OR-Tools

    Features:
    ✓ A very practical, hands-on Python book with several projects or case studies to build
    ✓ Come away with real-world templates that you may re-purpose for your own coding projects
    ✓ Written by an industry expert and teacher

    Who This Book Is For:
    Developers and students who already have prior experience in Python coding. Some prior mathematical experience or comfort level may be helpful as well.

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    Last edited by a moderator: Oct 3, 2020