{"id":2,"date":"2019-12-09T22:36:38","date_gmt":"2019-12-09T22:36:38","guid":{"rendered":"https:\/\/tinymlbook.com\/?page_id=2"},"modified":"2020-11-20T03:23:38","modified_gmt":"2020-11-20T03:23:38","slug":"home","status":"publish","type":"page","link":"https:\/\/tinymlbook.com\/","title":{"rendered":"TinyML Book"},"content":{"rendered":"\n
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\u201cThis is a must-read book for anyone interested in machine learning on resource-constrained devices. It is a milestone in the development of AI.\u201d<\/p>Massimo Banzi, Cofounder, Arduino<\/cite><\/blockquote>\n\n\n\n

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size\u2014small enough to run on a microcontroller. With this practical book you\u2019ll enter the field of TinyML<\/a>, where deep learning and embedded systems combine to make astounding things possible with tiny devices.<\/p>\n\n\n\n

Pete Warden<\/a> and Daniel Situnayake<\/a> 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 creating a series of TinyML projects, step-by-step.<\/p>\n\n\n\n

No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures.<\/p>\n\n\n\n