課程大綱： Tensorflow Lite for Microcontrollers培訓
Introduction
Microcontroller vs Microprocessor
Microcontrollers designed for machine learning tasks
Overview of TensorFlow Lite Features
On-device machine learning inference
Solving network latency
Solving power constraints
Preserving privacy
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'ss C++ Libraries
Coding the microcontroller
Collecting data
Running inference on the controller
Verifying the Results
Running a unit test to see the end-to-end workflow
Creating an Image Detection System
Classifying physical objects from image data
Creating TensorFlow model from scratch
Deploying an AI-enabled Device
Running inference on a microcontroller in the field
Troubleshooting
Summary and Conclusion
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