Synthesized answer
The provided passages do not contain information about what questions remain unanswered in the book "TinyML: Machine Learning on Arduino, ESP32, and other Pervasive Devices".
The passages describe the book's content, including its aim to introduce the field of TinyML, explain how to train small models, and guide readers through various TinyML projects using Arduino and ultra-low-power microcontrollers. They also mention the topics covered, such as speech recognition, person detection, gesture response, audio, image, and accelerometer data, as well as TensorFlow Lite for Microcontrollers and optimizing performance and security [1, 3]. However, they do not list any specific unanswered questions from the book itself.
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…