課程大綱： TensorFlow Lite for Embedded Linux培訓
Introduction
TensforFlow Lite's game changing role in embedded systems and IoT
Overview of TensorFlow Lite Features and Operations
Addressing limited device resources
Default and expanded operations
Setting up TensorFlow Lite
Installing the TensorFlow Lite interpreter
Installing other TensorFlow packages
Working from the command line vs Python API
Choosing a Model to Run on a Device
Overview of pre-trained models: image classification, object detection, smart reply, pose estimation, segmentation
Choosing a model from TensorFlow Hub or other source
Customizing a Pre-trained Model
How transfer learning works
Retraining an image classification model
Converting a Model
Understanding the TensorFlow Lite format (size, speed, optimizations, etc.)
Converting a model to the TensorFlow Lite format
Running a Prediction Model
Understanding how the model, interpreter, input data work together
Calling the interpreter from a device
Running data through the model to obtain predictions
Accelerating Model Operations
Understanding on-board acceleration, GPUs, etc.
Configuring Delegates to accelerate operations
Adding Model Operations
Using TensorFlow Select to add operations to a model.
Building a custom version of the interpreter
Using Custom operators to write or port new operations
Optimizing the Model
Understanding the balance of performance, model size, and accuracy
Using the Model Optimization Toolkit to optimize the size and performance of a model
Post-training quantization
Troubleshooting
Summary and Conclusion
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