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

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

At Google, Daniel Situnayake is in charge of developer advocacy for TensorFlow Lite. Tiny Farms, the first US company to use automation to generate insect protein on an industrial scale, was co-founded by him. He began his career as a lecturer at Birmingham City University, where he taught automatic identification and data acquisition.

Another best book on Tensorflow is TinyML. The size of deep learning networks is shrinking. Much, much smaller. The Google Assistant team has developed a model that is only 14 kilobytes in size and can run on a microcontroller. You'll enter the realm of TinyML with this practical book, where deep learning and embedded systems converge to make incredible things possible with tiny devices.

Pete Warden and Daniel Situnayake show how to train models that are small enough to fit into any setting. This guide leads you through constructing a series of TinyML projects, step by step, and is ideal for software and hardware engineers who wish to design embedded systems utilizing machine learning. No prior knowledge of machine learning or microcontrollers is required.

What you will learn:

  • Create a voice recognition system, a people-detecting camera, and a magic wand that responds to motions.
  • Use Arduino and ultra-low-power microcontrollers in your projects.
  • Learn the fundamentals of machine learning and how to create your own models.
  • Models should be trained to comprehend audio, picture, and accelerometer data.
  • Explore Google's TensorFlow Lite for Microcontrollers, a TinyML toolbox.
  • Debug programs and add privacy and security protections.
  • Latency, energy consumption, and model and binary size should all be optimized.

Some reviews about this book: “I am using the TinyML book to develop usable, hands-on competence with Tensorflow and machine learning. The book is a great starting point for learning this technology.”; “This is a fantastic, well-written, highly-entertaining resource for devs of all levels curious about running machine learning models on resource-limited devices and looking to play with edge computing.”

Authors: Pete Warden, Daniel Situnayake

Check price: https://www.amazon.com/TinyML-Learning-TensorFlow-Ultra-Low-Micro-Controllers/dp/1492052043?

Photo: https://www.tinyml.org/
Photo: https://www.tinyml.org/
Photo: https://towardsdatascience.com/
Photo: https://towardsdatascience.com/

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