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Course Outline
Introduction
- Differences between microcontrollers and microprocessors.
- Microcontrollers specifically designed for machine learning tasks.
Overview of TensorFlow Lite Features
- On-device machine learning inference.
- Addressing network latency issues.
- Overcoming power constraints.
- Preserving user privacy.
Constraints of a Microcontroller
- Energy consumption and physical size.
- Processing power, memory capacity, and storage limitations.
- Restricted operational capabilities.
Getting Started
- Setting up the development environment.
- Executing a simple "Hello World" example on the microcontroller.
Creating an Audio Detection System
- Acquiring a TensorFlow model.
- Converting the model to a TensorFlow Lite FlatBuffer format.
Serializing the Code
- Converting the FlatBuffer into a C byte array.
Working with Microcontroller C++ Libraries
- Programming the microcontroller.
- Collecting sensor data.
- Running inference on the controller.
Verifying the Results
- Conducting a unit test to observe the end-to-end workflow.
Creating an Image Detection System
- Classifying physical objects from image data.
- Building a TensorFlow model from scratch.
Deploying an AI-Enabled Device
- Executing inference on a microcontroller in the field.
Troubleshooting
Summary and Conclusion
Requirements
- Experience with C or C++ programming.
- A foundational understanding of Python.
- A general comprehension of embedded systems.
Audience
- Software 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
