TensorFlow Lite for Edge Devices - Tutorial

Tutorial on the TensorFlow Lite workflow: creating a TensorFlow or Keras model, converting it to a TFLite model, and validating its performance. It also introduces edge computing and its deployment challenges, and uses quantization to compress the model further while checking the resulting performance.

Syllabus

) Introduction.
) Why do we need TensorFlow Lite?.
) What is Edge Computing?.
) Why is Edge Computing gaining popularity?.
) Challenges in deploying models on Edge devices.
) What is TensorFlow Lite or TFLite?.
) TensorFlow Lite Workflow.
) Creating a TensorFlow or Keras model.
) Converting a TensorFlow or Keras model to TFLite.
) Validating the TFLite model performance.
) What is Quantization?.
) Compressing the TFLite model further.
) Compressing the TFLite model even further.
) Validating the most compressed TFLite model performance.
) Thank You.