I’ve been excited about running machine learning on microcontrollers ever since I joined Google and discovered the amazing work that the speech wakeword team were doing with 13 kilobyte models, and so I’m very pleased to finally be able to share a new O’Reilly book that shows you how to build your own TinyML applications. It took me and my colleague Dan most of 2019 to write it, and it builds on work from literally hundreds of contributors inside and outside Google. The most nerve-wracking part was the acknowledgements, since I knew that we’d miss important people, just because there were so many of them over such a long time. Thanks to everyone who helped, and we’re hoping this will just be the first of many guides to this area. There’s so much fascinating work that can now be done using ML with battery-powered or energy-harvesting devices, I can’t wait to see what the community comes up with!
To give you a taste, there’s a hundred-plus page preview of the first six chapters available as a free PDF, and I’m recording a series of screencasts to accompany the tutorials.
I have a question regarding the MagicWand project – on how to re-train the model with new accelerometer data.
I assume I should generate a new weights file for my model.
How should I modify the train.py to generate a new weights file ./netmodels/CNN/weights.h5″)
instead of using the default one provided in the sample code?
This book seems to be awesome, hope I will read it soon. But the cat on video is so cool! Such a good listener.