Synthesized answer
The central thesis of this text is that the fields of deep learning and embedded systems can be combined to achieve remarkable capabilities with tiny devices, a domain known as TinyML [Passage 1]. This is made possible by the increasing miniaturization of deep learning networks, allowing them to run on microcontrollers [Passage 3].
The book aims to guide software and hardware developers into this field, explaining how to train models small enough to operate within any environment [Passage 3]. It emphasizes practical application by walking readers through the creation of various TinyML projects, covering the essentials of machine learning, model training for different data types, and the use of specific toolkits like TensorFlow Lite for Microcontrollers [Passage 3].
Synthesized from the book passages below. Chat with the book on Feynman for follow-up.
From the book
Categories: Computers Pages: 478 Snippet: With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.
Title: TinyML by Pete Warden, Daniel Situnayake
Description: Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through…