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Raspberry Pi 3 Cookbook for Python Programmers 3E 상세페이지

Raspberry Pi 3 Cookbook for Python Programmers 3E

Unleash the potential of Raspberry Pi 3 with over 100 recipes

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전자책 정가
10,000원
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10,000원
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  • 2018.04.30 전자책 출간
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파일 정보
  • PDF
  • 542 쪽
  • 68.4MB
지원 환경
  • PC뷰어
  • PAPER
ISBN
9781788626989
ECN
-

이 작품의 시리즈더보기

  • [체험판] Raspberry Pi 3 Cookbook for Python Programmers 3E (Tim Cox, Dr. Steven Lawre)
  • Raspberry Pi 3 Cookbook for Python Programmers 3E (Tim Cox, Dr. Steven Lawre)
Raspberry Pi 3 Cookbook for Python Programmers 3E

작품 정보

▶Book Description
Raspberry Pi 3 Cookbook for Python Programmers – Third Edition begins by guiding you through setting up Raspberry Pi 3, performing tasks using Python 3.6, and introducing the first steps to interface with electronics. As you work through each chapter, you will build your skills and apply them as you progress. You will learn how to build text classifiers, predict sentiments in words, develop applications using the popular Tkinter library, and create games by controlling graphics on your screen. You will harness the power of a built in graphics processor using Pi3D to generate your own high-quality 3D graphics and environments.

You will understand how to connect Raspberry Pi's hardware pins directly to control electronics, from switching on LEDs and responding to push buttons to driving motors and servos. Get to grips with monitoring sensors to gather real-life data, using it to control other devices, and viewing the results over the internet. You will apply what you have learned by creating your own Pi-Rover or Pi-Hexipod robots. You will also learn about sentiment analysis, face recognition techniques, and building neural network modules for optical character recognition.

Finally, you will learn to build movie recommendations system on Raspberry Pi 3.

▶What You Will Learn
⦁ Learn to set up and run Raspberry Pi 3
⦁ Build text classifiers and perform automation using Python
⦁ Predict sentiments in words and create games and graphics
⦁ Detect edges and contours in images
⦁ Build human face detection and recognition system
⦁ Use Python to drive hardware
⦁ Sense and display real-world data
⦁ Build a neural network module for optical character recognition
⦁ Build movie recommendations system

▶Key Features
⦁ Leverage the power of Raspberry Pi 3 using Python programming
⦁ Create 3D games, build neural network modules, and interface with your own circuits
⦁ Packed with clear, step-by-step recipes to walk you through the capabilities of Raspberry Pi

▶Who This Book Is For
This book is for anyone who wants to master the skills of Python programming using Raspberry Pi 3. Prior knowledge of Python will be an added advantage.

▶What this book covers
⦁ Chapter 1, Getting Started with a Raspberry Pi Computer, introduces the Raspberry Pi and explores the various ways in which it can be set up and used.

⦁ Chapter 2, Dividing Text Data and Building a Text Classifier, guides us to build a text classifier; it can classify text using the bag-of-words model.

⦁ Chapter 3, Using Python for Automation and Productivity, explains how to use graphical user interfaces to create your own applications and utilities.

⦁ Chapter 4, Predicting Sentiments in Words, explains how Naive Bayes classifiers and logistic regression classifiers are constructed to analyze the sentiment in words.

⦁ Chapter 5, Creating Games and Graphics, explains how to create a drawing application and graphical games using the Tkinter canvas.

⦁ Chapter 6, Detecting Edges and Contours in Images, describes in detail how images are loaded, displayed, and saved. It provides detailed implementations of erosion and dilation, image segmentation, histogram equalization, edge detection, detecting corners in images, and more.

⦁ Chapter 7, Creating 3D Graphics, discusses how we can use the hidden power of the Raspberry Pi's graphical processing unit to learn about 3D graphics and landscapes, and produce our very own 3D maze for exploration.

⦁ Chapter 8, Building Face Detector and Face Recognition Applications, explains how human faces can be detected from webcams and recognized using images stored in a database.

⦁ Chapter 9, Using Python to Drive Hardware, establishes the fact that to experience the Raspberry Pi at its best, we really have to use it with our own electronics. This chapter discusses how to create circuits with LEDs and switches, and how to use them to indicate the status of a system and provide control. Finally, it shows us how to create our own game controller, light display, and a persistence-of-vision text display.

⦁ Chapter 10, Sensing and Displaying Real-World Data, explains how to use an analog-todigital converter to provide sensor readings to the Raspberry Pi. We discover how to store and graph the data in real time, as well as display it on an LCD text display. Next, we record the data in a SQL database and display it in our own web server. Finally, we transfer the data to the internet, which will allow us to view and share the captured data anywhere in the world.

⦁ Chapter 11, Building a Neural Network Module for Optical Character Recognition, introduces neural network implementation on Raspberry Pi 3. Optical characters are detected, displayed, and recognized using neural networks.

⦁ Chapter 12, Building Robots, takes you through building two different types of robot (a Rover-Pi and a Pi-Bug), plus driving a servo-based robot arm. We look at motor and servo control methods, using sensors, and adding a compass sensor for navigation.

⦁ Chapter 13, Interfacing with Technology, teaches us how to use the Raspberry Pi to trigger remote mains sockets, with which we can control household appliances. We learn how to communicate with the Raspberry Pi over a serial interface and use a smartphone to control everything using Bluetooth. Finally, we look at creating our own applications to control USB devices.

⦁ Chapter 14, Can I Recommend a Movie for You?, explains how movie recommender systems are built. It elaborates how Euclidean distance and Pearson correlation scores are computed. It also explains how similar users are found in the dataset and the movie recommender module is built.

⦁ Appendix, Hardware and Software List, explains the detailed hardware software list used inside the book.

작가 소개

⦁ Tim Cox
Tim Cox lives in England with his wife and two young daughters and works as a software engineer. His passion for programming stems from a Sinclair Spectrum that sparked his interest in computers and electronics. At university, he earned a BEng in Electronics and Electrical Engineering, and into a career in developing embedded software for a range of industries.

Supporting the vision behind the Raspberry Pi, to encourage a new generation of engineers, Tim co-founded the MagPi magazine (the official magazine for the Raspberry Pi) and produces electronic kits through his site PiHardware.

⦁ Dr. Steven Lawrence Fernandes
Dr. Steven Lawrence Fernandes has Postdoctoral Research experience working in the area of Deep Learning at The University of Alabama at Birmingham, USA. He has received the prestigious US award from Society for Design and Process Science for his outstanding service contributions in 2017 and Young Scientist Award by Vision Group on Science and Technology in 2014. He has also received Research Grant from The Institution of Engineers.

He has completed his B.E (Electronics and Communication Engineering) and M.Tech (Microelectronics) and Ph.D. (Computer Vision and Machine Learning). His Ph.D work Match Composite Sketch with Drone Images has received patent notification (Patent Application Number: 2983/CHE/2015).

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