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Course Outline
Introduction
- Microcontroller vs. Microprocessor
- Microcontrollers tailored for machine learning tasks
Overview of TensorFlow Lite Features
- On-device machine learning inference
- Addressing network latency issues
- Managing power constraints
- Ensuring data privacy
Constraints of a Microcontroller
- Energy consumption and physical size
- Processing power, memory, and storage limitations
- Restricted operational capabilities
Getting Started
- Setting up the development environment
- Executing a basic 'Hello World' programme on the microcontroller
Creating an Audio Detection System
- Obtaining a TensorFlow model
- Converting the model into a TensorFlow Lite FlatBuffer
Serializing the Code
- Transforming the FlatBuffer into a C byte array
Working with the Microcontroller's C++ Libraries
- Coding the microcontroller
- Collecting data
- Performing inference on the controller
Verifying the Results
- Executing a unit test to demonstrate the end-to-end workflow
Creating an Image Detection System
- Classifying physical objects from image data
- Developing a TensorFlow model from scratch
Deploying an AI-enabled Device
- Performing inference on a microcontroller in the field
Troubleshooting
Summary and Conclusion
Requirements
- Experience with C or C++ programming
- Basic understanding of Python
- General knowledge of embedded systems
Target Audience
- Developers
- Programmers
- Data scientists interested in embedded systems development
21 Hours
Testimonials (2)
The trainer was very interactive and steadily paced.
Carolyn Yaacoby - Yeshiva University
Course - Raspberry Pi for Beginners
Just getting off the ground and doing some basic things was super useful
