Introduction To Computation And Programming Using Python

Author: John V. Guttag
Publisher: MIT Press
ISBN: 0262529629
Size: 48.88 MB
Format: PDF, ePub, Docs
View: 4623
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: 46.80 MB
Format: PDF, ePub, Docs
View: 7639
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 Journal of Statistics and Medical

Introduction To Computational Social Science

Author: Claudio Cioffi-Revilla
Publisher: Springer
ISBN: 3319501313
Size: 77.14 MB
Format: PDF
View: 6554
This textbook provides a comprehensive and reader-friendly introduction to the field of computational social science (CSS). Presenting a unified treatment, the text examines in detail the four key methodological approaches of automated social information extraction, social network analysis, social complexity theory, and social simulation modeling. This updated new edition has been enhanced with numerous review questions and exercises to test what has been learned, deepen understanding through problem-solving, and to practice writing code to implement ideas. Topics and features: contains more than a thousand questions and exercises, together with a list of acronyms and a glossary; examines the similarities and differences between computers and social systems; presents a focus on automated information extraction; discusses the measurement, scientific laws, and generative theories of social complexity in CSS; reviews the methodology of social simulations, covering both variable- and object-oriented models.

How To Design Programs

Author: Matthias Felleisen
Publisher: MIT Press
ISBN: 9780262062183
Size: 25.35 MB
Format: PDF
View: 3888
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.

Introduction To Computing And Programming In Python

Author: Mark Guzdial
Publisher: Prentice Hall
ISBN: 0131176552
Size: 69.33 MB
Format: PDF, Mobi
View: 2937
Guzdial introduces programming as a way of creating and manipulating mediaa context familiar and intriguing to today's readers.Starts readers with actual programming early on. Puts programming in a relevant context (Computing for Communications). Includes implementing Photoshop-like effects, reversing/splicing sounds, creating animations. Acknowledges that readers in this audience care about the Web; introduces HTML and covers writing programs that generate HTML. Uses the Web as a Data Source; shows readers how to read from files, but also how to write programs to directly read Web pages and distill information from there for use in other calculations, other Web pages, etc. (examples include temperature from a weather page, stock prices from a financials page).A comprehensive guide for anyone interested in learning the basics of programming with one of the best web languages, Python.

Python Programming

Author: John M. Zelle
Publisher: Franklin, Beedle & Associates, Inc.
ISBN: 1887902996
Size: 58.23 MB
Format: PDF, ePub
View: 1933
This book is suitable for use in a university-level first course in computing (CS1), as well as the increasingly popular course known as CS0. It is difficult for many students to master basic concepts in computer science and programming. A large portion of the confusion can be blamed on the complexity of the tools and materials that are traditionally used to teach CS1 and CS2. This textbook was written with a single overarching goal: to present the core concepts of computer science as simply as possible without being simplistic.

Introduction To Computing Problem Solving With Python

Author: Jeeva Jose
ISBN: 9382609814
Size: 26.63 MB
Format: PDF
View: 674
This book 'Introduction to Computing and Problem Solving with Python' will help every student,teacher and researcher to understand the computing basics and advanced PythonProgramming language. The Python programming topics include the reserved keywords,identifiers, variables, operators, data types and their operations, flowcontrol techniques which include decision making and looping, modules, filesand exception handling techniques. Advanced topics like Python regularexpressions, Database Programming and Object Oriented Programming concepts arealso covered in detail. All chapters have worked out programs, illustrations,review and frequently asked interview questions. The simple style of presentationmakes this a friend for self-learners. More than 300 solved lab exercisesavailable in this book is tested in Python 3.4.3 version for Windows. The book covers syllabus for more than 35 International Universities and45 Indian universities like Dr. APJ Abdul Kalam Technological University,Christ University, Savitribai Phule Pune University, University of Delhi, University of Calicut, Mahatma Gandhi University, University of Mumbai, AICTE, CBSE, MIT, University of Virginia, University of Chicago, University of Toronto, Technical University of Denmark etc.

Scientific Computing With Python 3

Author: Claus Fuhrer
Publisher: Packt Publishing Ltd
ISBN: 1786463644
Size: 40.92 MB
Format: PDF, Kindle
View: 5813
An example-rich, comprehensive guide for all of your Python computational needs About This Book Your ultimate resource for getting up and running with Python numerical computations Explore numerical computing and mathematical libraries using Python 3.x code with SciPy and NumPy modules A hands-on guide to implementing mathematics with Python, with complete coverage of all the key concepts Who This Book Is For This book is for anyone who wants to perform numerical and mathematical computations in Python. It is especially useful for developers, students, and anyone who wants to use Python for computation. Readers are expected to possess basic a knowledge of scientific computing and mathematics, but no prior experience with Python is needed. What You Will Learn The principal syntactical elements of Python The most important and basic types in Python The essential building blocks of computational mathematics, linear algebra, and related Python objects Plot in Python using matplotlib to create high quality figures and graphics to draw and visualize your results Define and use functions and learn to treat them as objects How and when to correctly apply object-oriented programming for scientific computing in Python Handle exceptions, which are an important part of writing reliable and usable code Two aspects of testing for scientific programming: Manual and Automatic In Detail Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more. Style and approach This book takes a concept-based approach to the language rather than a systematic introduction. It is a complete Python tutorial and introduces computing principles, using practical examples to and showing you how to correctly implement them in Python. You'll learn to focus on high-level design as well as the intricate details of Python syntax. Rather than providing canned problems to be solved, the exercises have been designed to inspire you to think about your own code and give you real-world insight.

Practical Programming

Author: Jennifer Campbell
ISBN: 9781934356272
Size: 34.40 MB
Format: PDF
View: 947
Welcome to computer science in the 21st century. Did you ever wonder how computers represent DNA? How they can download a web page containing population data and analyze it to spot trends? Or how they can change the colors in a color photograph? If so, this book is for you. By the time you're done, you'll know how to do all of that and a lot more. And Python makes it easy and fun. Computers are used in every part of science from ecology to particle physics. This introduction to computer science continually reinforces those ties by using real-world science problems as examples. Anyone who has taken a high school science class will be able to follow along as the book introduces the basics of programming, then goes on to show readers how to work with databases, download data from the web automatically, build graphical interfaces, and most importantly, how to think like a professional programmer. Topics covered include: Basic elements of programming from arithmetic to loops and if statements. Using functions and modules to organize programs. Using lists, sets, and dictionaries to organize data. Designing algorithms systematically. Debugging things when they go wrong. Creating and querying databases. Building graphical interfaces to make programs easier to use. Object-oriented programming and programming patterns.