Course Outline
Introduction
- Microcontroller vs Microprocessor
- Microcontrollers designed for machine learning tasks
Overview of TensorFlow Lite Features
- On-device machine learning inference
- Solving network latency
- Solving power constraints
- Preserving privacy
Constraints of a Microcontroller
- Energy consumption and size
- Processing power, memory, and storage
- Limited operations
Getting Started
- Preparing the development environment
- Running a simple Hello World on the Microcontroller
Creating an Audio Detection System
- Obtaining a TensorFlow Model
- Converting the Model to a TensorFlow Lite FlatBuffer
Serializing the Code
- Converting the FlatBuffer to a C byte array
Working with Microcontroller'ss C++ Libraries
- Coding the microcontroller
- Collecting data
- Running inference on the controller
Verifying the Results
- Running a unit test to see the end-to-end workflow
Creating an Image Detection System
- Classifying physical objects from image data
- Creating TensorFlow model from scratch
Deploying an AI-enabled Device
- Running inference on a microcontroller in the field
Troubleshooting
Summary and Conclusion
Requirements
- C or C++ programming experience
- A basic understanding of Python
- A general understanding of embedded systems
Audience
- Developers
- Programmers
- Data scientists with an interest in embedded systems development
Testimonials (4)
Just getting off the ground and doing some basic things was super useful
Remy Pieron - Facebook
Course - Arduino Programming for Beginners
The trainer was very interactive and steadily paced.
Carolyn Yaacoby - Yeshiva University
Course - Raspberry Pi for Beginners
The knowledge of the trainer. He was able to answer all of my questions, even questions about our platform. He also continued to help until we all understood the material.
James O'Donnell - Tennant Company
Course - Embedded Linux Kernel and Driver Development
The details on how compiler behaves depending on to the syntax usage. The "Quiz" sections are very stimulating