Learning TinyML

Offered By: LinkedIn Learning

Tags

TinyML Courses Machine Learning Courses Embedded Systems Courses Quantization Courses Edge Computing Courses Model Optimization Courses

Course Description

Overview

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

Syllabus

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

Taught by

Vaidheeswaran Archana

Related Courses

Embedded Systems - Shape The World: Microcontroller Input/Output
The University of Texas at Austin via edX Model Checking
Chennai Mathematical Institute via Swayam Introduction to the Internet of Things and Embedded Systems
University of California, Irvine via Coursera Sistemas embebidos: Aplicaciones con Arduino
Universidad Nacional Autónoma de México via Coursera Quantitative Formal Modeling and Worst-Case Performance Analysis
EIT Digital via Coursera