Course Outline

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

Requirements

  • An understanding of deep learning concepts
  • Python programming experience
  • A device running embedded Linux (Raspberry Pi, Coral device, etc.)

Audience

  • Developers
  • Data scientists with an interest in embedded systems
 21 Hours

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