Ashwin Pajankar – författare

Build an inexpensive cluster of multiple Raspberry Pi computers and install all the required libraries to write parallel and scientific programs in Python 3. This book covers setting up your Raspberry Pis, installing the necessary software, and making a cluster of multiple Pis. Once the cluster is built, its power has to be exploited by means of programs to run on it. So, Raspberry Pi Supercomputing and Scientific Programming teaches you to code the cluster with the MPI4PY library of Python 3. Along the way, you will learn the concepts of the Message Passing Interface (MPI) standards and will explore the fundamentals of parallel programming on your inexpensive cluster. This will make this book a great starting point for supercomputing enthusiasts who want to get started with parallel programming. The book finishes with details of symbolic mathematics and scientific and numerical programming in Python, using SymPi, SciPy, NumPy, and Matplotlib. You’ll see how to process signals and images, carry out calculations using linear algebra, and visualize your results, all using Python code. With the power of a Raspberry Pi supercomputer at your fingertips, data-intensive scientific programming becomes a reality at home.What You Will Learn  Discover the essentials of supercomputingBuild a low-cost cluster of Raspberry Pis at homeHarness the power of parallel programming and the Message Passing Interface (MPI)Use your Raspberry Pi for symbolic, numerical, and scientific programmingWho This Book Is ForPython 3 developers who seek the knowledge of parallel programming, Raspberry Pi enthusiasts, researchers, and the scientific Python community.

Get a quick start to learn, understand, and apply GNU Octave using a math- and programming-friendly approach. This book focuses on an end-to-end track to teach mathematical programming, data science, signal processing, and image processing with GNU Octave.GNU Octave by Example starts with an introduction to GNU Octave, a free and open-source alternative to MATLAB. Next, it explains the processes to install GNU Octave on popular operating systems such as Windows, Ubuntu, Raspberry Pi, and other platforms. Further, it covers hands-on exercises with GNU Octave exploring the basic functionality and command line in interactive mode. This is followed by covering matrices and various operations including how to read and analyze data from various sources. Moving forward, it introduces commonly used programming constructs in data visualization. It explains 2D and 3D data visualization along with data analysis. It also demonstrates the concepts related to geometry and its application with GNU Octave. It concludes with coverage of signal processing followed by image, video, and audio processing techniques.After reading this book, you will be able to write your own programs for scientific and numerical applications.What You Will Learn  ●        Understand the practical aspects of GNU Octave with math and programming-friendly abstractions●        Install GNU Octave on multiple platforms including Windows, Raspberry Pi, and Ubuntu●        Work with GNU Octave using the GUI, the command line, and Jupyter notebooks●        Implement 2D and 3D data visualization and analysis with GNU OctaveWho This Book Is For Software engineers, data engineers, data science enthusiasts, and computer vision professionals.

Quickly start programming with Python 3 for data visualization with this step-by-step, detailed guide. This book’s programming-friendly approach using libraries such as leather, NumPy, Matplotlib, and Pandas will serve as a template for business and scientific visualizations.You’ll begin by installing Python 3, see how to work in Jupyter notebook, and explore Leather, Python’s popular data visualization charting library. You’ll also be introduced to the scientific Python 3 ecosystem and work with the basics of NumPy, an integral part of that ecosystem. Later chapters are focused on various NumPy routines along with getting started with Scientific Data visualization using matplotlib. You’ll review the visualization of 3D data using graphs and networks and finish up by looking at data visualization with Pandas, including the visualization of COVID-19 data sets.The code examples are tested on popular platforms like Ubuntu, Windows, and Raspberry Pi OS. WithPractical Python Data Visualization you’ll master the core concepts of data visualization with Pandas and the Jupyter notebook interface.What You'll LearnReview practical aspects of Python Data Visualization with programming-friendly abstractions Install Python 3 and Jupyter on multiple platforms including Windows, Raspberry Pi, and Ubuntu Visualize COVID-19 data sets with PandasWho This Book Is ForData Science enthusiasts and professionals, Business analysts and managers, software engineers, data engineers.

Quickly start programming with Linux while learning the Raspberry Pi OS—the Linux distribution designed specifically for low-cost Raspberry Pis.

Learn the core aspects of NumPy, Matplotlib, and Pandas, and use them to write programs with Python 3. This book focuses heavily on various data visualization techniques and will help you acquire expert-level knowledge of working with Matplotlib, a MATLAB-style plotting library for Python programming language that provides an object-oriented API for embedding plots into applications. You'll begin with an introduction to Python 3 and the scientific Python ecosystem. Next, you'll explore NumPy and ndarray data structures, creation routines, and data visualization. You'll examine useful concepts related to style sheets, legends, and layouts, followed by line, bar, and scatter plots. Chapters then cover recipes of histograms, contours, streamplots, and heatmaps, and how to visualize images and audio with pie and polar charts. Moving forward, you'll learn how to visualize with pcolor, pcolormesh, and colorbar, and how to visualize in 3D in Matplotlib, create simple animations, and embed Matplotlib with different frameworks. The concluding chapters cover how to visualize data with Pandas and Matplotlib, Seaborn, and how to work with the real-life data and visualize it. After reading Hands-on Matplotlib you'll be proficient with Matplotlib and able to comfortably work with ndarrays in NumPy and data frames in Pandas. What You'll LearnUnderstand Data Visualization and Python using MatplotlibReview the fundamental data structures in NumPy and Pandas Work with 3D plotting, visualizations, and animationsVisualize images and audio dataWho This Book Is ForData scientists, machine learning engineers and software professionals with basic programming skills.

Learn how to automate unit tests of Python 3 with automation libraries, such as doctest, unittest, nose, nose2, pytest, and selenium. This book explores important concepts in software test automation and demonstrates how to automate, organize, and execute unit tests with Python. It also introduces readers to the concepts of web browser automation and logging.This new edition starts with an introduction to Python 3. Next, it covers doctest and pydoc. This is followed by a discussion on unittest, a framework that comes packaged with Python 3 itself. There is a dedicated section on creating test suites, followed by an explanation of how nose2 provides automatic test module discovery. Moving forward, you will learn about pytest, the most popular third-party library and testrunner for Python. You will see how to write and execute tests with pytest. You’ll also learn to discover tests automatically with pytest.This edition features two brand new chapters, the firstof which focuses on the basics of web browser automation with Selenium. You’ll learn how to use Selenium with unittest to write test cases for browser automation and use the Selenium IDE with web browsers such as Chrome and Firefox. You’ll then explore logging frameworks such as Python’s built-in logger and the third-party framework loguru.The book concludes with an exploration of test-driven development with pytest, during which you will execute a small project using TDD methodology.What You Will LearnStart testing with doctest and unittestUnderstand the idea of unit testingGet started with nose 2 and pytestLearn how to use logger and loguruWork with Selenium and test driven development Who This Book Is ForPython developers, software testers, open source enthusiasts, and contributors to the Python community.

Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios.The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoreticaland practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch.After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage. What You'll LearnReview data structures in NumPy and Pandas Demonstrate machine learning techniques and algorithmUnderstand supervised learning and unsupervised learning Examine convolutional neural networks and Recurrent neural networksGet acquainted with scikit-learn and PyTorchPredict sequences in recurrent neural networks and long short term memory Who This Book Is ForData scientists, machine learning engineers, and software professionals with basic skills in Python programming.

Understand the concepts of image processing with Python 3 and create applications using Raspberry Pi 4. This book covers image processing with the latest release of Python 3, using Raspberry Pi OS and Raspberry Pi 4B with the 8 GB RAM model as the preferred computing platform.This second edition begins with the installation of Raspberry Pi OS on the latest model of Raspberry Pi and then introduces Python programming language, IDEs for Python, and digital image processing. It also illustrates the theoretical foundations of Image processing followed by advanced operations in image processing. You'll then review image processing with NumPy, and Matplotlib followed by transformations, interpolation, and measurements of images. Different types of filters such as Kernels convolution filters, low pass filters, high pass filters, and Fourier filters are discussed in a clear, methodical manner. Additionally, the book examines variousimage processing techniques such as Morphology, Thresholding, and Segmentation, followed by a chapter on live webcam input with OpenCV, an image processing library with Python. The book concludes with an appendix covering a new library for image processing with Python, pgmagik, followed by a few important tips and tricks relevant to RPi.What You'll LearnGet started with Raspberry Pi and PythonUnderstand Image Processing with PillowSee how image processing is processed using Numpy and MatplotlibUse Pi camera and webcamWho This Book Is ForRaspberry Pi and IoT enthusiasts, and Python and Open Source professionals

Perform a wide variety of computer vision tasks such as image processing and manipulation, feature and object detection, and image restoration to build real-life computer vision applicationsKey FeaturesExplore the potential of computer vision with Raspberry Pi and Python programmingPerform computer vision tasks such as image processing and manipulation using OpenCV and Raspberry PiDiscover easy-to-follow examples and screenshots to implement popular computer vision techniques and applicationsBook DescriptionRaspberry Pi is one of the popular single-board computers of our generation. All the major image processing and computer vision algorithms and operations can be implemented easily with OpenCV on Raspberry Pi. This updated second edition is packed with cutting-edge examples and new topics, and covers the latest versions of key technologies such as Python 3, Raspberry Pi, and OpenCV. This book will equip you with the skills required to successfully design and implement your own OpenCV, Raspberry Pi, and Python-based computer vision projects.At the start, you'll learn the basics of Python 3, and the fundamentals of single-board computers and NumPy. Next, you'll discover how to install OpenCV 4 for Python 3 on Raspberry Pi, before covering major techniques and algorithms in image processing, manipulation, and computer vision. By working through the steps in each chapter, you'll understand essential OpenCV features. Later sections will take you through creating graphical user interface (GUI) apps with GPIO and OpenCV. You'll also learn to use the new computer vision library, Mahotas, to perform various image processing operations. Finally, you'll explore the Jupyter Notebook and how to set up a Windows computer and Ubuntu for computer vision.By the end of this book, you'll be able to confidently build and deploy computer vision apps.What you will learnSet up a Raspberry Pi for computer vision applicationsPerform basic image processing with libraries such as NumPy, Matplotlib, and OpenCVDemonstrate arithmetical, logical, and other operations on imagesWork with a USB webcam and the Raspberry Pi Camera ModuleImplement low-pass and high-pass filters and understand their applications in image processingCover advanced techniques such as histogram equalization and morphological transformationsCreate GUI apps with Python 3 and OpenCVPerform machine learning with K-means clustering and image quantizationWho this book is forThis book is for beginners as well as experienced Raspberry Pi and Python 3 enthusiasts who are looking to explore the amazing world of computer vision. Working knowledge of the Python 3 programming language is assumed.

Develop real-world interactive physical computing applications using the Micro:bit with more than 100 MicroPython examples with circuit diagramsKey FeaturesExplore hardware programming and leverage the power of the BBC Micro:bit and MicroPythonBuild real-life creative projects step by step with each chapter increasing in complexity and interactivityLearn the features of the Micro:bit and how to interface it with electronic components through circuits and code examplesBook DescriptionThis book is a one-stop guide for learning BBC Micro:bit with MicroPython, exploring many hardware components and programming techniques to provide detailed insights into developing practical applications with the Micro:bit. It will also show you how hardware components can be manipulated using a combination of Micro:bit and MicroPython for developing practical projects. BBC Micro:bit in Practice will help you gain a holistic understanding of the BBC Micro:bit platform and MicroPython programming, guiding you through mini projects aimed at developing practical knowledge of circuit design and writing programs. You’ll learn how to write programs for working with built-in LEDs and buttons, interfacing external LEDs, buttons, motors, buzzers, and much more. You’ll also work with built-in radio, speakers, accelerometer, and a compass. You’ll dive into concepts related to the Micro:bit filesystem, interfacing external displays, and working with libraries in detail before exploring sewable circuits and wearable technology.After reading this Micro:bit book, you’ll understand how to apply principles in electronics and MicroPython to create interesting real-life projects from scratch.What you will learnExplore the essentials of the BBC Micro:bit, its hardware specifications, and versionsProgram built-in and external LEDs and buttonsProgram external LED-based displays, buzzers, and stepper motorsUse analog input and pulse width modulation to drive a servo motorDetect gestures with internal sensors such as accelerometer, compass, and magnetometerWork on projects with built-in features such as music, speech, radio, and filesystemExplore the advanced features of the Micro:bitWho this book is forThis book is for anyone who wants to use the combination of MicroPython and the BBC Micro:bit for building exciting real-life projects. Engineers, researchers, hobbyists, and technology instructors working in areas such as embedded systems, electronics, software development, IoT, robotics, teaching, and training will find this book useful for building projects. Prior experience with building basic electronic circuits and any programming language, not necessarily MicroPython, will be helpful.