Introduction To Computation And Programming Using Python

Author: John V. Guttag
Publisher: MIT Press
ISBN: 0262529629
Size: 13.49 MB
Format: PDF, Docs
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The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization.

Deep Learning Models And Its Application An Overview With The Help Of R Software Second In Series Machine Learning

Author: Editor IJSMI
Publisher: International Journal of Statistics and Medical Informatics
ISBN: 1796489034
Size: 64.84 MB
Format: PDF
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Deep Learning Models and its application: An overview with the help of R softwarePrefaceDeep learning models are widely used in different fields due to its capability to handle large and complex datasets and produce the desired results with more accuracy at a greater speed. In Deep learning models, features are selected automatically through the iterative process wherein the model learns the features by going deep into the dataset and selects the features to be modeled. In the traditional models the features of the dataset needs to be specified in advance. The Deep Learning algorithms are derived from Artificial Neural Network concepts and it is a part of broader Machine Learning Models. This book intends to provide an overview of Deep Learning models, its application in the areas of image recognition & classification, sentiment analysis, natural language processing, stock market prediction using R statistical software package, an open source software package. The book also includes an introduction to python software package which is also open source software for the benefit of the users.This books is a second book in series after the author’s first book- Machine Learning: An Overview with the Help of R Software https://www.amazon.com/dp/B07KQSN447EditorInternational Journal of Statistics and Medical Informaticswww.ijsmi.com/book.php

Handbook Of Research On Software For Gifted And Talented School Activities In K 12 Classrooms

Author: Ikuta, Shigeru
Publisher: IGI Global
ISBN: 1799814025
Size: 80.29 MB
Format: PDF, ePub
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As technology continues to play a pivotal role in society, education is a field that has become heavily influenced by these advancements. New learning methods are rapidly emerging and being implemented into classrooms across the world using software that is low cost and easy to handle. These tools are crucial in creating skillful learning techniques in classrooms, yet there is a lack of information and research on the subject. The Handbook of Research on Software for Gifted and Talented School Activities in K-12 Classrooms is an essential reference source that discusses newly developed but easy-to-handle and less costly software and tools and their implementation in real 21st-century classrooms worldwide. The book also helps and supports teachers to conduct gifted and talented school activities in K-12 classrooms. Featuring research on topics such as educational philosophy and skillful learning techniques, this book is ideally designed for software developers, educators, researchers, psychologists, instructional designers, curriculum developers, principals, academicians, and students seeking coverage on the emerging role that newly developed software plays in early education.

Introduction To Computational Social Science

Author: Claudio Cioffi-Revilla
Publisher: Springer Science & Business Media
ISBN: 1447156617
Size: 16.48 MB
Format: PDF, Mobi
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This reader-friendly textbook is the first work of its kind to provide a unified Introduction to Computational Social Science (CSS). Four distinct methodological approaches are examined in detail, namely automated social information extraction, social network analysis, social complexity theory and social simulation modeling. The coverage of these approaches is supported by a discussion of the historical context, as well as by a list of texts for further reading. Features: highlights the main theories of the CSS paradigm as causal explanatory frameworks that shed new light on the nature of human and social dynamics; explains how to distinguish and analyze the different levels of analysis of social complexity using computational approaches; discusses a number of methodological tools; presents the main classes of entities, objects and relations common to the computational analysis of social complexity; examines the interdisciplinary integration of knowledge in the context of social phenomena.

How To Design Programs

Author: Matthias Felleisen
Publisher: MIT Press
ISBN: 9780262062183
Size: 67.22 MB
Format: PDF, ePub, Mobi
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Processing simple forms of data - Processing arbitrarily large data - More on processing arbitrarily large data - Abstracting designs - Generative recursion - Changing the state of variables - Changing compound values.

Exploratory Programming For The Arts And Humanities

Author: Nick Montfort
Publisher: MIT Press
ISBN: 0262034204
Size: 34.69 MB
Format: PDF, Mobi
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A book for anyone who wants to learn programming to explore and create, with exercises and projects to help the reader learn by doing.

Modeling Techniques In Predictive Analytics With Python And R

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 013389214X
Size: 53.84 MB
Format: PDF, Kindle
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Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

A Gentle Introduction To Effective Computing In Quantitative Research

Author: Harry J. Paarsch
Publisher: MIT Press
ISBN: 0262034115
Size: 37.67 MB
Format: PDF
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This book offers a practical guide to the computational methods at the heart of most modern quantitative research. It will be essential reading for research assistants needing hands-on experience; students entering PhD programs in business, economics, and other social or natural sciences; and those seeking quantitative jobs in industry. No background in computer science is assumed; a learner need only have a computer with access to the Internet. Using the example as its principal pedagogical device, the book offers tried-and-true prototypes that illustrate many important computational tasks required in quantitative research. The best way to use the book is to read it at the computer keyboard and learn by doing. The book begins by introducing basic skills: how to use the operating system, how to organize data, and how to complete simple programming tasks. For its demonstrations, the book uses a UNIX-based operating system and a set of free software tools: the scripting language Python for programming tasks; the database management system SQLite; and the freely available R for statistical computing and graphics. The book goes on to describe particular tasks: analyzing data, implementing commonly used numerical and simulation methods, and creating extensions to Python to reduce cycle time. Finally, the book describes the use of LaTeX, a document markup language and preparation system.

Learn Programming With Python

Author: Ana Bell
Publisher: Pearson Professional
ISBN: 9781617293788
Size: 54.10 MB
Format: PDF, ePub, Docs
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Summary Get Programming: Learn to code with Python introduces you to the world of writing computer programs without drowning you in confusing jargon or theory that make getting started harder than it should be. Filled with practical examples and step-by-step lessons using the easy-on-the-brain Python language, this book will get you programming in no time! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Let''s face it. The only way to learn computer programming is to do it. Whether you want to skill up for your next job interview or just get a few pet projects done, programming can be an amazing tool. This book is designed especially for beginners, helping them learn to program hands on, step by step, project by project. It''s time to get programming! About the Book Get Programming: Learn to code with Python teaches you the basics of computer programming using the Python language. In this exercise-driven book, you''ll be doing something on nearly every page as you work through 38 compact lessons and 7 engaging capstone projects. By exploring the crystal-clear illustrations, exercises that check your understanding as you go, and tips for what to try next, you''ll start thinking like a programmer in no time. What''s Inside Programming skills you can use in any language Learn to code--no experience required Learn Python, the language for beginners Dozens of exercises and examples help you learn by doing About the Reader No prior programming experience needed.. About the Author Ana Bell is an MIT lecturer and scientist who teaches the popular course, Introduction to Computer Science and Programming Using Python. Table of Contents LEARNING HOW TO PROGRAM Lesson 1 - Why should you learn how to program? Lesson 2 - Basic principles of learning a programming language UNIT 1 - VARIABLES, TYPES, EXPRESSIONS, AND STATEMENTS Lesson 3 - Introducing Python: a programming language Lesson 4 - Variables and expressions: giving names and values to things Lesson 5 - Object types and statements of code 46 Lesson 6 - Capstone project: your first Python program-convert hours to minutes UNIT 2 - STRINGS, TUPLES, AND INTERACTING WITH THE USER Lesson 7 - Introducing string objects: sequences of characters Lesson 8 - Advanced string operations Lesson 9 - Simple error messages Lesson 10 - Tuple objects: sequences of any kind of object Lesson 11 - Interacting with the user Lesson 12 - Capstone project: name mashup UNIT 3 - MAKING DECISIONS IN YOUR PROGRAMS Lesson 13 - Introducing decisions in programs Lesson 14 - Making more-complicated decisions Lesson 15 - Capstone project: choose your own adventure UNIT 4 - REPEATING TASKS Lesson 16 - Repeating tasks with loops Lesson 17 - Customizing loops Lesson 18 - Repeating tasks while conditions hold Lesson 19 - Capstone project: Scrabble, Art Edition UNIT 5 - ORGANIZING YOUR CODE INTO REUSABLE BLOCKS Lesson 20 - Building programs to last Lesson 21 - Achieving modularity and abstraction with functions Lesson 22 - Advanced operations with functions Lesson 23 - Capstone project: analyze your friends UNIT 6 - WORKING WITH MUTABLE DATA TYPES Lesson 24 - Mutable and immutable objects Lesson 25 - Working with lists Lesson 26 - Advanced operations with lists Lesson 27 - Dictionaries as maps between objects Lesson 28 - Aliasing and copying lists and dictionaries Lesson 29 - Capstone project: document similarity UNIT 7 - MAKING YOUR OWN OBJECT TYPES BY USING OBJECT-ORIENTED PROGRAMMING Lesson 30 - Making your own object types Lesson 31 - Creating a class for an object type Lesson 32 - Working with your own object types Lesson 33 - Customizing classes Lesson 34 - Capstone project: card game UNIT 8 - USING LIBRARIES TO ENHANCE YOUR PROGRAMS Lesson 35 - Useful libraries Lesson 36 - Testing and debugging your programs Lesson 37 - A library for graphical user interfaces Lesson 38 - Capstone project: game of tag Appendix A - Answers to lesson exercises Appendix B - Python cheat sheet Appendix C - Interesting Python libraries