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Introduction to Programming Concepts with Case Studies in Python PDF Download

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Introduction to Programming Concepts with Case Studies in Python

Introduction to Programming Concepts with Case Studies in Python PDF Author: Göktürk Üçoluk
Publisher: Springer Science & Business Media
ISBN: 3709113431
Category : Computers
Languages : en
Pages : 222

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Book Description

The current text provides a clear introduction to Computer Science concepts in a programming environment. It is designed as suitable use in freshman- or introductory level coursework in CS and provides the fundamental concepts as well as abstract theorems for solving computational problems. The Python language serves as a medium for illustrating and demonstrating the concepts.

Introduction to Programming Concepts with Case Studies in Python

Introduction to Programming Concepts with Case Studies in Python PDF Author: Göktürk Üçoluk
Publisher: Springer Science & Business Media
ISBN: 3709113431
Category : Computers
Languages : en
Pages : 222

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Book Description
The current text provides a clear introduction to Computer Science concepts in a programming environment. It is designed as suitable use in freshman- or introductory level coursework in CS and provides the fundamental concepts as well as abstract theorems for solving computational problems. The Python language serves as a medium for illustrating and demonstrating the concepts.

Introduction to Programming Concepts with Case Studies in Python

Introduction to Programming Concepts with Case Studies in Python PDF Author: Springer
Publisher:
ISBN: 9783709113448
Category :
Languages : en
Pages : 232

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Book Description


IP Cores Design from Specifications to Production

IP Cores Design from Specifications to Production PDF Author: Khaled Salah Mohamed
Publisher: Springer
ISBN: 3319220357
Category : Technology & Engineering
Languages : en
Pages : 154

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Book Description
This book describes the life cycle process of IP cores, from specification to production, including IP modeling, verification, optimization, and protection. Various trade-offs in the design process are discussed, including those associated with many of the most common memory cores, controller IPs and system-on-chip (SoC) buses. Readers will also benefit from the author’s practical coverage of new verification methodologies. such as bug localization, UVM, and scan-chain. A SoC case study is presented to compare traditional verification with the new verification methodologies. Discusses the entire life cycle process of IP cores, from specification to production, including IP modeling, verification, optimization, and protection; Introduce a deep introduction for Verilog for both implementation and verification point of view. Demonstrates how to use IP in applications such as memory controllers and SoC buses. Describes a new verification methodology called bug localization; Presents a novel scan-chain methodology for RTL debugging; Enables readers to employ UVM methodology in straightforward, practical terms.

Machine Learning for Decision Sciences with Case Studies in Python

Machine Learning for Decision Sciences with Case Studies in Python PDF Author: S. Sumathi
Publisher: CRC Press
ISBN: 1000590933
Category : Mathematics
Languages : en
Pages : 476

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Book Description
This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning. This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.

Introduction to Computing Using Python

Introduction to Computing Using Python PDF Author: Ljubomir Perkovic
Publisher: John Wiley & Sons
ISBN: 1118890949
Category : Computers
Languages : en
Pages : 480

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Book Description
Perkovic's Introduction to Computing Using Python: An Application Development Focus, 2nd Edition is more than just an introduction to programming. It is an inclusive introduction to Computer Science that takes the pedagogical approach of "the right tool for the job at the right moment," and focuses on application development. The approach is hands-on and problem-oriented, with practice problems and solutions appearing throughout the text. The text is imperative-first, but does not shy away from discussing objects early where appropriate. Discussions of user-defined classes and Object-Oriented Programming appear later in the text, when students have more background and concepts can be motivated. Chapters include an introduction to problem solving techniques and classical algorithms, problem-solving and programming and ways to apply core skills to application development. This edition also includes examples and practice problems provided within a greater variety of domains. It also includes case studies integrated into additional chapters, providing students with real life applications using the concepts and tools covered in the chapters.

Data Science Concepts and Techniques with Applications

Data Science Concepts and Techniques with Applications PDF Author: Usman Qamar
Publisher: Springer Nature
ISBN: 9811561338
Category : Computers
Languages : en
Pages : 196

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Book Description
This book comprehensively covers the topic of data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three sections: The first section is an introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics. Followed by discussion on wide range of applications of data science and widely used techniques in data science. The second section is devoted to the tools and techniques of data science. It consists of data pre-processing, feature selection, classification and clustering concepts as well as an introduction to text mining and opining mining. And finally, the third section of the book focuses on two programming languages commonly used for data science projects i.e. Python and R programming language. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. The book is suitable for both undergraduate and postgraduate students as well as those carrying out research in data science. It can be used as a textbook for undergraduate students in computer science, engineering and mathematics. It can also be accessible to undergraduate students from other areas with the adequate background. The more advanced chapters can be used by postgraduate researchers intending to gather a deeper theoretical understanding.

Artificial Intelligence in Education

Artificial Intelligence in Education PDF Author: Carolyn Penstein Rosé
Publisher: Springer
ISBN: 3319938460
Category : Computers
Languages : en
Pages : 580

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Book Description
This two volume set LNAI 10947 and LNAI 10948 constitutes the proceedings of the 19th International Conference on Artificial Intelligence in Education, AIED 2018, held in London, UK, in June 2018.The 45 full papers presented in this book together with 76 poster papers, 11 young researchers tracks, 14 industry papers and 10 workshop papers were carefully reviewed and selected from 192 submissions. The conference provides opportunities for the cross-fertilization of approaches, techniques and ideas from the many fields that comprise AIED, including computer science, cognitive and learning sciences, education, game design, psychology, sociology, linguistics as well as many domain-specific areas.

ECEL2015-14th European Conference on e-Learning,

ECEL2015-14th European Conference on e-Learning, PDF Author: Amanda Jefferies and Marija Cubric
Publisher: Academic Conferences and publishing limited
ISBN: 1910810703
Category : Education
Languages : en
Pages : 852

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Book Description
These Proceedings represent the work of contributors to the 14th European Conference on e-Learning, ECEL 2015, hosted this year by the University of Hertfordshire, Hatfield, UK on 29-30 October 2015. The Conference and Programme Co-Chairs are Pro-fessor Amanda Jefferies and Dr Marija Cubric, both from the University of Hertfordshire. The conference will be opened with a keynote address by Professor Patrick McAndrew, Director, Institute of Educational Tech-nology, Open University, UK with a talk on "Innovating for learning: designing for the future of education." On the second day the keynote will be delivered by Professor John Traxler, University of Wolverhampton, UK on the subject of "Mobile Learning - No Longer Just e-Learning with Mobiles." ECEL provides a valuable platform for individuals to present their research findings, display their work in progress and discuss conceptual advances in many different branches of e-Learning. At the same time, it provides an important opportunity for members of the EL community to come together with peers, share knowledge and exchange ideas. With an initial submission of 169 abstracts, after the double blind, peer review process there are 86 academic papers,16 Phd Papers, 5 Work in Progress papers and 1 non academic papers in these Conference Proceedings. These papers reflect the truly global nature of research in the area with contributions from Algeria, Australia, Austria, Belgium, Botswana, Canada, Chile, Cov-entry, Czech Republic, Denmark, Egypt, England, Estonia, France, Germany, Ireland, Japan, Kazakhstan, New Zealand, Nigeria, Norway, Oman, Portugal, Republic of Kazakhstan, Romania, Saudi Arabia, Scotland, Singapore, South Africa, Sweden, the Czech Republic, Turkey, Uganda, UK, United Arab Emirates, UK and USA, Zimbabwe. A selection of papers - those agreed by a panel of reviewers and the editor will be published in a special conference edition of the EJEL (Electronic Journal of e-Learning www.ejel.org ).

Introduction to Computational Models with Python

Introduction to Computational Models with Python PDF Author: Jose M. Garrido
Publisher: CRC Press
ISBN: 1498712045
Category : Computers
Languages : en
Pages : 496

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Book Description
Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such as the Numpy and Scipy m

Introduction to Data Science

Introduction to Data Science PDF Author: Laura Igual
Publisher: Springer
ISBN: 3319500171
Category : Computers
Languages : en
Pages : 218

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Book Description
This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

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