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Course Outline
Introduction
- Microcontrollers vs. Microprocessors
- Microcontrollers tailored for machine learning tasks
Overview of TensorFlow Lite Features
- On-device machine learning inference
- Addressing network latency
- Overcoming power constraints
- Ensuring privacy preservation
Microcontroller Constraints
- Energy consumption and physical size
- Processing power, memory, and storage limitations
- Limited operational capabilities
Getting Started
- Setting up the development environment
- Executing a basic 'Hello World' example on the Microcontroller
Building an Audio Detection System
- Obtaining a TensorFlow Model
- Converting the model to a TensorFlow Lite FlatBuffer
Code Serialization
- Transforming the FlatBuffer into a C byte array
Utilizing Microcontroller C++ Libraries
- Programming the microcontroller
- Data collection
- Running inference on the controller
Verifying Results
- Running a unit test to demonstrate the end-to-end workflow
Developing an Image Detection System
- Classifying physical objects from image data
- Creating a TensorFlow model from scratch
Deploying an AI-Enabled Device
- Performing inference on a microcontroller in field conditions
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
