TensorFlow Lite for Embedded Linux Training Course

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