This course aims to teach learners about the intersection of Machine Learning (ML) and the Internet of Things (IoT), focusing on Edge ML. The learning outcomes include understanding the benefits of bringing ML closer to IoT devices, enabling quicker insights, tighter security, and predictive actions. The course covers topics such as the state of the art in Edge ML and IoT, practical ways for developers to start working with these technologies, and tools like TensorFlow Lite. The teaching method involves exploring concepts, discussing practical applications, and demonstrating tools like Google Coral and Raspberry Pi. This course is intended for developers interested in leveraging ML for IoT applications, particularly those looking to enhance security and efficiency by implementing Edge ML solutions.
Overview
Syllabus
Intro
Zeke
Washing Machine
ML vs Deep Learning
The Streetlight Problem
Human vs Machine Learning
Discipline of Machine Learning
Machine Learning Tools
Example
Train predict tune loop
Train predict model
Train predict classifier
Deployment
Pretrained APIs
Azure Face API
Cloud vs Local Performance
The IoT is the source of data
Volume
The problem
The solution
What we want
What does this look like
What you get instead
Google Coral
Google Coral Demo
IoT
Raspberry Pie
Lowcost ML
Microcontrollers
Federated Learning
Three Big Things
Single Board Computers
Tensorflow Light
Taught by
NDC Conferences
