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Course Outline
Introduction
- Microcontroller vs Microprocessor.
- Microcontrollers designed for machine learning tasks.
Overview of TensorFlow Lite Features
- On-device machine learning inference.
- Addressing network latency.
- Addressing power constraints.
- Ensuring privacy preservation.
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 C++ Libraries
- Coding the microcontroller.
- Collecting data.
- Running inference on the controller.
Verifying the Results
- Running a unit test to demonstrate the end-to-end workflow.
Creating an Image Detection System
- Classifying physical objects from image data.
- Creating 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.
- Basic understanding of Python.
- General knowledge 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
