Donald J. Norris - Machine Learning with the Raspberry Pi: Experiments with Data and Computer Vision (Technology in Action)
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- Book:Machine Learning with the Raspberry Pi: Experiments with Data and Computer Vision (Technology in Action)
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Machine Learning with the Raspberry Pi: Experiments with Data and Computer Vision (Technology in Action): summary, description and annotation
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Using the Pi Camera and a Raspberry Pi board, expand and replicate interesting machine learning (ML) experiments. This book provides a solid overview of ML and a myriad of underlying topics to further explore. Non-technical discussions temper complex technical explanations to make the hottest and most complex topic in the hobbyist world of computing understandable and approachable.
Machine learning, also commonly referred to as deep learning (DL), is currently being integrated into a multitude of commercial products as well as widely being used in industrial, medical, and military applications. It is hard to find any modern human activity, which has not been touched by artificial intelligence (AI) applications. Building on the concepts first presented in Beginning Artificial Intelligence with the Raspberry Pi, youll go beyond simply understanding the concepts of AI into working with real machine learning experiments and applying practical deep learning concepts to experiments with the Pi board and computer vision.
What you learn with Machine Learning with the Raspberry Pi can then be moved on to other platforms to go even further in the world of AI and ML to better your hobbyist or commercial projects.
What Youll Learn
- Acquire a working knowledge of current ML
- Use the Raspberry Pi to implement ML techniques and algorithms
- Apply AI and ML tools and techniques to your own work projects and studies
Who This Book Is For
Engineers and scientists but also experienced makers and hobbyists. Motivated high school students who desire to learn about ML can benefit from this material with determination.
Donald J. Norris: author's other books
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Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-5173-7 . For more detailed information, please visit http://www.apress.com/source-code .
is an avid electronics hobbyist and maker. He is also an electronics engineer with an advanced degree in Production Management. Don is retired from civilian government service with the US Navy, where he specialized in acoustics and digital signal processing. He also has more than a dozen years experience as a professional software developer using C, C#, C++, Python, and Java, and 5 years experience as a certified IT security consultant.
is a machine learning engineer who holds B.Sc. and M.Sc. in Information Technology. Ahmed is a teaching assistant and a researcher who is interested in machine/deep learning, computer vision, and Python. He is a machine learning technical reviewer and consultant helping others do their projects. Ahmed contributes written tutorials and articles to a number of blogs including Paperspace , Real Python , KDnuggets , Heartbeat , and Towards Data Science .
Ahmed has authored three books titled TensorFlow: A Guide to Build Artificial Neural Networks Using Python(Lambert 2017) , Practical Computer Vision Applications Using Deep Learning with CNNs(Apress, 2018) , and Building Android Apps in Python Using Kivy with Android Studio(Apress, 2019) .
He welcomes you to connect with him through LinkedIn ( linkedin.com/in/AhmedFGad ), Facebook ( fb.com/AhmedFGadd ), and e-mail ( ahmed.f.gad@gmail.com ).
This chapter will provide you with introductions to both RasPi and ML. The RasPi discussion will be first, which will help you understand the hardware platform that will be used to run all the books demonstrations. An introductory ML discussion follows, which will provide you with a framework to comprehend what ML is all about and why it is such an exciting and rapidly evolving field of study.
Raspberry Pi 3 Model B+
There are a few differences between the RasPi 3 Model B and B+. They are basically the same except that the B+ has a slightly faster processor clock speed increase and has some improvements in the wireless functions as compared to the B model. Neither of these improvements will have a significant impact on running this books projects if you use a B instead of a B+ model.
I will not discuss what constitutes a RasPi single-board computer because that is already adequately covered by many available books and blogs. As mentioned earlier, I used a RasPi 3 Model B+ in a workstation configuration. This setup is where a RasPi is connected with a USB keyboard, USB mouse, and HDMI monitor. In my setup, the RasPi is powered by a 2.2A, 5V supply with a micro USB connector.
The RasPi does not use a mechanical disk drive for implementing a file system which includes an operating system (OS). All recent RasPi versions rely on using a pluggable micro SD card to serve as the secondary storage. While it is possible to connect a traditional disk drive to a RasPi, it will only serve as an auxiliary storage device and not as the primary storage for the OS or as the bootable partition. I will next show you how to download and install an OS on a micro SD card in order to enable the RasPi to serve as a functional ML microcontroller.
Undoubtedly the simplest way to get up and running is to purchase a preprogrammed micro SD card. Such cards are ready to go and only need to be configured to match your particular workstation configuration, including your WiFi network. The WiFi configuration process will be discussed in a later section, but first I would like to discuss how to create your own micro SD card if you so desire.
The micro SD card software to be loaded is known as a Raspbian Image and is available without charge from many online web sites, with my recommended site being the Raspberry Pi Foundation site at raspberrypi.org . The latest OS Image is always available from the Downloads section of the web site. There are two types of the OS Image that you can download. The first type is named NOOBS, which is an abbreviation for New Out of the Box Software. There are two versions of NOOBS available. One version is named NOOBS, and the other version is named NOOBS Lite. Both versions are identified as v3.0.0 as of the time of this writing. NOOBS has an easy operating system installer which contains the Raspbian OS as well as another popular OS named LibreELEC . Additionally, the NOOBS version provides a selection of alternative operating systems which are subsequently downloaded from the Internet and installed. NOOBS Lite contains the same operating system installer without Raspbian pre-loaded and no LibreELEC option. However, this version provides the same operating system selection menu allowing Raspbian and other OS Images to be downloaded and installed.
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