Computer Programming Theory Practicals
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Computer Programming Theory And Practicals
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Author : Satyendra Singh Chouhan
language : en
Publisher: KHANNA PUBLISHING HOUSE
Release Date : 2025-06-01
Computer Programming Theory And Practicals written by Satyendra Singh Chouhan and has been published by KHANNA PUBLISHING HOUSE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-01 with Computers categories.
Computer Programming Theory Practicals
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Author : Mr. Rohit Manglik
language : en
Publisher: EduGorilla Publication
Release Date : 2024-06-20
Computer Programming Theory Practicals written by Mr. Rohit Manglik and has been published by EduGorilla Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-20 with Computers categories.
Programming fundamentals are covered. Guides students to analyze coding techniques, fostering expertise in software development through practical projects and theoretical study.
A Practical Theory Of Programming
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Author : Eric C.R. Hehner
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-09-08
A Practical Theory Of Programming written by Eric C.R. Hehner and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-08 with Computers categories.
There are several theories of programming. The first usable theory, often called "Hoare's Logic", is still probably the most widely known. In it, a specification is a pair of predicates: a precondition and postcondition (these and all technical terms will be defined in due course). Another popular and closely related theory by Dijkstra uses the weakest precondition predicate transformer, which is a function from programs and postconditions to preconditions. lones's Vienna Development Method has been used to advantage in some industries; in it, a specification is a pair of predicates (as in Hoare's Logic), but the second predicate is a relation. Temporal Logic is yet another formalism that introduces some special operators and quantifiers to describe some aspects of computation. The theory in this book is simpler than any of those just mentioned. In it, a specification is just a boolean expression. Refinement is just ordinary implication. This theory is also more general than those just mentioned, applying to both terminating and nonterminating computation, to both sequential and parallel computation, to both stand-alone and interactive computation. And it includes time bounds, both for algorithm classification and for tightly constrained real-time applications.
Practical Foundations For Programming Languages
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Author : Robert Harper
language : en
Publisher: Cambridge University Press
Release Date : 2016-04-04
Practical Foundations For Programming Languages written by Robert Harper and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-04 with Computers categories.
This book unifies a broad range of programming language concepts under the framework of type systems and structural operational semantics.
Concepts In Programming Languages
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Author : John C. Mitchell
language : en
Publisher: Cambridge University Press
Release Date : 2003
Concepts In Programming Languages written by John C. Mitchell and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computers categories.
A comprehensive undergraduate textbook covering both theory and practical design issues, with an emphasis on object-oriented languages.
Practical Machine Learning With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Apress
Release Date : 2017-12-20
Practical Machine Learning With Python written by Dipanjan Sarkar and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-20 with Computers categories.
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries andframeworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
What Can Be Computed
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Author : John MacCormick
language : en
Publisher: Princeton University Press
Release Date : 2018-05-01
What Can Be Computed written by John MacCormick and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-01 with Computers categories.
An accessible and rigorous textbook for introducing undergraduates to computer science theory What Can Be Computed? is a uniquely accessible yet rigorous introduction to the most profound ideas at the heart of computer science. Crafted specifically for undergraduates who are studying the subject for the first time, and requiring minimal prerequisites, the book focuses on the essential fundamentals of computer science theory and features a practical approach that uses real computer programs (Python and Java) and encourages active experimentation. It is also ideal for self-study and reference. The book covers the standard topics in the theory of computation, including Turing machines and finite automata, universal computation, nondeterminism, Turing and Karp reductions, undecidability, time-complexity classes such as P and NP, and NP-completeness, including the Cook-Levin Theorem. But the book also provides a broader view of computer science and its historical development, with discussions of Turing's original 1936 computing machines, the connections between undecidability and Gödel's incompleteness theorem, and Karp's famous set of twenty-one NP-complete problems. Throughout, the book recasts traditional computer science concepts by considering how computer programs are used to solve real problems. Standard theorems are stated and proven with full mathematical rigor, but motivation and understanding are enhanced by considering concrete implementations. The book's examples and other content allow readers to view demonstrations of—and to experiment with—a wide selection of the topics it covers. The result is an ideal text for an introduction to the theory of computation. An accessible and rigorous introduction to the essential fundamentals of computer science theory, written specifically for undergraduates taking introduction to the theory of computation Features a practical, interactive approach using real computer programs (Python in the text, with forthcoming Java alternatives online) to enhance motivation and understanding Gives equal emphasis to computability and complexity Includes special topics that demonstrate the profound nature of key ideas in the theory of computation Lecture slides and Python programs are available at whatcanbecomputed.com
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Verified Software Theories Tools Experiments
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Author : Bertrand Meyer
language : en
Publisher: Springer
Release Date : 2008-06-29
Verified Software Theories Tools Experiments written by Bertrand Meyer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-29 with Computers categories.
A Step Towards Verified Software Worries about the reliability of software are as old as software itself; techniques for allaying these worries predate even James King’s 1969 thesis on “A program verifier. ” What gives the whole topic a new urgency is the conjunction of three phenomena: the blitz-like spread of software-rich systems to control ever more facets of our world and our lives; our growing impatience with deficiencies; and the development—proceeding more slowly, alas, than the other two trends—of techniques to ensure and verify software quality. In 2002 Tony Hoare, one of the most distinguished contributors to these advances over the past four decades, came to the conclusion that piecemeal efforts are no longer sufficient and proposed a “Grand Challenge” intended to achieve, over 15 years, the production of a verifying compiler: a tool that while processing programs would also guarantee their adherence to specified properties of correctness, robustness, safety, security and other desirable properties. As Hoare sees it, this endeavor is not a mere research project, as might normally be carried out by one team or a small consortium of teams, but a momentous endeavor, comparable in its scope to the successful mission to send a man to the moon or to the sequencing of the human genome.
Cognitively Informed Systems Utilizing Practical Approaches To Enrich Information Presentation And Transfer
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Author : Alkhalifa, Eshaa
language : en
Publisher: IGI Global
Release Date : 2006-01-31
Cognitively Informed Systems Utilizing Practical Approaches To Enrich Information Presentation And Transfer written by Alkhalifa, Eshaa and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-31 with Technology & Engineering categories.
"This book identifies the main areas of cognitive science and for each area, how different system designs benefit from the findings made in that area"--Provided by publisher.