Learn the basics of TinyML, the field of machine learning that enables ML applications to run on handheld and IoT devices.
Overview
Syllabus
Introduction
- Getting started with TinyML
- What is TinyML?
- What you should know
- Defining constraints
- Checklist for a TinyML problem
- Pre-trained models
- Quantization and types of quantization
- TFLite post training quantization
- Quantization awareness training in TFLite
- Pruning
- Knowledge distillation
- Challenge: Compare results of optimization
- Solution: Compare results of optimization
- Edge impulse
- Deploy a classification project to your phone
- Challenge: Using voice recognition on your phone
- Solution: Using voice recognition on your phone
- Hardware devices
- Bringing all the concepts together
- Resources for TinyML
- Future of the TinyML: Research directions
- Next steps with TinyML
Taught by
Vaidheeswaran Archana
