Learning TinyML

Learn the basics of TinyML, the field of machine learning that enables ML applications to run on handheld and IoT devices.

Introduction

• Getting started with TinyML
• What is TinyML?
• What you should know

1. Is Your Problem a TinyML Problem?

• Defining constraints
• Checklist for a TinyML problem

2. Solving the Constraints: Optimization Techniques

• 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

3. Deploying TinyML Models

• Edge impulse
• Deploy a classification project to your phone
• Challenge: Using voice recognition on your phone
• Solution: Using voice recognition on your phone

4. TinyMLOps

• Hardware devices
• Bringing all the concepts together

Conclusion

• Resources for TinyML
• Future of the TinyML: Research directions
• Next steps with TinyML