  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