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Course Outline
Introduction
- Microcontroller versus Microprocessor
- Microcontrollers optimized for machine learning tasks
Overview of TensorFlow Lite Features
- On-device machine learning inference
- Mitigating network latency
- Addressing power constraints
- Preserving data privacy
Constraints of a Microcontroller
- Energy consumption and physical size
- Processing power, memory, and storage limitations
- Limited computational operations
Getting Started
- Preparing the development environment
- Executing a simple Hello World example 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's C++ Libraries
- Programming the microcontroller
- Collecting data
- Running 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
- Running inference on a microcontroller in the field
Troubleshooting
Summary and Conclusion
Requirements
- Experience with C or C++ programming
- Foundational knowledge of Python
- General understanding of embedded systems
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
