TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers 1st Edition

4.5 out of 5 stars 63 ratings
ISBN-13: 978-1492052043
ISBN-10: 1492052043
Why is ISBN important?
ISBN
This bar-code number lets you verify that you're getting exactly the right version or edition of a book. The 13-digit and 10-digit formats both work.
Scan an ISBN with your phone
Use the Amazon App to scan ISBNs and compare prices.
Have one to sell?
<Embed>
Loading your book clubs
There was a problem loading your book clubs. Please try again.
Not in a club? Learn more
Amazon book clubs early access

Join or create book clubs

Choose books together

Track your books
Bring your club to Amazon Book Clubs, start a new book club and invite your friends to join, or find a club that’s right for you for free.
Used: Like New | Details
Sold by Reuseaworld
Condition: Used: Like New
Comment: This book is in mint condition, any imperfection will be trivial.
Access codes and supplements are not guaranteed with used items.
7 used from $31.96
In Stock.
Ships from and sold by Amazon.com.
Available at a lower price from other sellers that may not offer free Prime shipping.
Fastest delivery: Thursday, Oct 29 Details
List Price: $49.99
Save: $14.09 (28%)
25 new from $35.49

TinyML: Machine Learning ... has been added to your Cart

Available at a lower price from other sellers that may not offer free Prime shipping.

"Devoted" by Dean Koontz
For the first time in paperback, from Dean Koontz, the master of suspense, comes an epic thriller about a terrifying killer and the singular compassion it will take to defeat him. | Learn more
click to open popover

Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.

  • Apple
    Apple
  • Android
    Android
  • Windows Phone
    Windows Phone
  • Click here to download from Amazon appstore
    Android

To get the free app, enter your mobile phone number.

kcpAppSendButton

Frequently bought together

  • TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
  • +
  • Building Machine Learning Powered Applications: Going from Idea to Product
  • +
  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Total price: $114.57
Buy the selected items together

Special offers and product promotions


From the Publisher

What Hardware Do You Need?

You’ll need a laptop or desktop computer with a USB port. This will be your main programming environment, where you edit and compile the programs that you run on the embedded device. You’ll connect this computer to the embedded device using the USB port and a specialized adapter that will depend on what development hardware you’re using. The main computer can be running Windows, Linux, or macOS. For most of the examples we train our machine learning models in the cloud, using Google Colab, so don’t worry about having a specially equipped computer.

You will also need an embedded development board to test your programs on. To do something interesting you’ll need a microphone, accelerometers, or a camera attached, and you want something small enough to build into a realistic prototype project, along with a battery. This was tough to find when we started this book, so we worked together with the chip manufacturer Ambiq and maker retailer SparkFun to produce the $15 SparkFun Edge board. All of the book’s examples will work with this device.

What Software Do You Need?

All of the projects in this book are based around the TensorFlow Lite for Microcontrollers framework. This is a variant of the TensorFlow Lite framework designed to run on embedded devices with only a few tens of kilobytes of memory available. All of the projects are included as examples in the library, and it’s open source, so you can find it on GitHub.

You’ll need some kind of editor to examine and modify your code. If you’re not sure which one you should use, Microsoft’s free VS Code application is a great place to start. It works on macOS, Linux, and Windows, and has a lot of handy features like syntax highlighting and autocomplete. If you already have a favorite editor you can use that, instead; we won’t be doing extensive modifications for any of our projects.

You’ll also need somewhere to enter commands. On macOS and Linux this is known as the terminal, and you can find it in your Applications folder under that name. On Windows it’s known as the Command Prompt, which you can find in your Start menu.

There will also be extra software that you’ll need to communicate with your embedded development board, but this will depend on what device you have. If you’re using either the SparkFun Edge board or an Mbed device, you’ll need to have Python installed for some build scripts, and then you can use GNU Screen on Linux or macOS or Tera Term on Windows to access the debug logging console, showing text output from the embedded device. If you have an Arduino board, everything you need is installed as part of the IDE, so you just need to download the main software package.

Editorial Reviews

About the Author

Pete Warden is technical lead for mobile and embedded TensorFlow. He was CTO and founder of Jetpac, which was acquired by Google in 2014, and previously worked at Apple. He was a founding member of the TensorFlow team, and blogs about practical deep learning at https://petewarden.com.



Daniel Situnayake leads developer advocacy for TensorFlow Lite at Google. He co-founded Tiny Farms, the first US company using automation to produce insect protein at industrial scale. He began his career lecturing in automatic identification and data capture at Birmingham City University.


Product details

  • Item Weight : 1.75 pounds
  • Paperback : 504 pages
  • ISBN-13 : 978-1492052043
  • Product Dimensions : 7 x 1.01 x 9.19 inches
  • ISBN-10 : 1492052043
  • Publisher : O'Reilly Media; 1st Edition (December 31, 2019)
  • Language: : English
  • Customer Reviews:
    4.5 out of 5 stars 63 ratings

Customer reviews

4.5 out of 5 stars
4.5 out of 5
64 global ratings
How are ratings calculated?

Top reviews from the United States

Reviewed in the United States on February 12, 2020
Verified Purchase
11 people found this helpful
Comment Report abuse
Reviewed in the United States on August 1, 2020
Verified Purchase
3 people found this helpful
Comment Report abuse
Reviewed in the United States on May 23, 2020
Verified Purchase
Reviewed in the United States on June 28, 2020
Verified Purchase
Reviewed in the United States on June 15, 2020
Verified Purchase
Reviewed in the United States on September 8, 2020
Verified Purchase

Top reviews from other countries

Ben Cook
5.0 out of 5 stars Great introduction to Embedded AI
Reviewed in the United Kingdom on May 11, 2020
Verified Purchase
4 people found this helpful
Report abuse
K Seunarine
4.0 out of 5 stars Broken links and mistakes in code.
Reviewed in the United Kingdom on May 7, 2020
Verified Purchase
3 people found this helpful
Report abuse
Steve Karmeinsky
5.0 out of 5 stars Edge computing is the future
Reviewed in the United Kingdom on July 29, 2020
Verified Purchase
Scott Y.
5.0 out of 5 stars Excellent intro
Reviewed in the United Kingdom on October 22, 2020
Verified Purchase
Florian aus München
2.0 out of 5 stars A great idea but published too early, still very experimental
Reviewed in Germany on August 6, 2020
Verified Purchase