Search results for "Introduction to Human-Computer Interaction"
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R 493.00
Functional Start to Computing with Python
A Functional Start to Computing with Python enables students to quickly learn computing without having to use loops, variables, and object abstractions at the start. Requiring no prior programming experience, the book draws on Python’s flexible data types and operations as well as its capacity for defining new functions. Along with the specifics of Python, the text covers important concepts of computing, including software engineering motivation, algorithms behind syntax rules, advanced functional programming ideas, and, briefly, finite state machines.Taking a student-friendly, interactive approach to teach computing, the book addresses more difficult concepts and abstractions later in the text. The author presents ample explanations of data types, operators, and expressions. He also describes comprehensions—the powerful specifications of lists and dictionaries—before introducing loops and variables. This approach helps students better understand assignment syntax and iteration by giving them a mental model of sophisticated data first.Web ResourceThe book’s supplementary website at http://functionalfirstpython.com/ provides many ancillaries, including: Interactive flashcards on Python language elementsLinks to extra support for each chapterUnit testing and programming exercisesAn interactive Python stepper toolChapter-by-chapter points Material for lectures
R 5,147.43
Introduction to Interactive Programming on the Internet
This unique book provides an introduction to programming on the Internet. Class-tested over a two-year period, this text covers the "nuts and bolts" of Internet programming. In addition to core fundamentals, readers are introduced to Web page construction HTML, managing an account on a web server, client-server model, and JavaScript programming.
R 3,853.00
Computer Science Programming Basics with Ruby
This book is intended as a text for an "Introduction to Computer Science" course for non-majors. That is, this book is intended for "the rest of the world"; namely "every can program". No specific pre-requisites at the college level are needed. However, basic high school level mathematics knowledge is assumed. Our approach minimizes the need to harp on the illustrative programming language details in favor of an immediate introduction to core computer science topics. User interface issues are ignored as these have little if anything to do with the foundations of Computer Science. We use a very practical programming language (Ruby) as the foundation and provide a variety of detailed examples using the language that illustrate the concepts being taught.
R 1,211.00
Learning IPython for Interactive Computing and Data Visualization -
Get started with Python for data analysis and numerical computing in the Jupyter notebookAbout This Book• Learn the basics of Python in the Jupyter Notebook• Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn• Perform highly-efficient numerical computations with Numba, Cython, and ipyparallelWho This Book Is ForThis book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know.What You Will Learn• Install Anaconda and code in Python in the Jupyter Notebook• Load and explore datasets interactively• Perform complex data manipulations effectively with pandas• Create engaging data visualizations with matplotlib and seaborn• Simulate mathematical models with NumPy• Visualize and process images interactively in the Jupyter Notebook with scikit-image• Accelerate your code with Numba, Cython, and IPython.parallel• Extend the Notebook interface with HTML, JavaScript, and D3In DetailPython is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors.This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.Style and approachThis is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook.
R 1,531.00
IPython Interactive Computing and Visualization Cookbook
Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists...Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.
R 1,889.00
Introduction to Computational Models with Python
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 modules. The Python source code and data files are available on the author’s website.The book’s five sections present: An overview of problem solving and simple Python programs, introducing the basic models and techniques for designing and implementing problem solutions, independent of software and hardware toolsProgramming principles with the Python programming language, covering basic programming concepts, data definitions, programming structures with flowcharts and pseudo-code, solving problems, and algorithmsPython lists, arrays, basic data structures, object orientation, linked lists, recursion, and running programs under LinuxImplementation of computational models with Python using Numpy, with examples and case studies The modeling of linear optimization problems, from problem formulation to implementation of computational modelsThis book introduces the principles of computational modeling as well as the approaches of multi- and interdisciplinary computing to beginners in the field. It provides the foundation for more advanced studies in scientific computing, including parallel computing using MPI, grid computing, and other methods and techniques used in high-performance computing.
R 6,704.27
Practical Computer Vision with SimpleCV
SimpleCV is a cross platform (Windows, Macintosh, Linux) framework in Python that makes writing computer vision applications quick and easy. This book takes you through real world examples and applications using SimpleCV so that anyone, even with zero knowledge of computer vision, can use to create their own vision application in a few minutes with a webcam and PC. SimpleCV has its own integrated shell (command line) and code editor so you can rapidly test new techniques, load and run examples, view documentation, etc. The library also works with webcams, cell phones, the Xbox Kinect, and TI's Beagle Board and Panda Board. There are 1-click installers for every platform, so you can be up and running any of the examples included in this book in a few minutes.
R 865.00
Introduction to Machine Learning with Python
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills
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R 1,369.00
R 457.00
Introduction to Search with Sphinx
This concise introduction to Sphinx shows you how to use this free software to index an enormous number of documents and provide fast results to both simple and complex searches. Written by the creator of Sphinx, this authoritative book is short and to the point. * Understand the particular way Sphinx conducts searches * Install and configure Sphinx, and run a few basic tests * Issue basic queries to Sphinx at the application level * Learn the syntax of search text and the effects of various search options * Get strategies for dealing with large data sets, such as multi-index searching * Apply relevance and ranking guidelines for presenting best results to the user
R 1,038.00
