This course teaches learners how to use TensorFlow Lite, an open-source deep learning framework for on-device inference. The course covers the importance of TensorFlow Lite, edge computing, challenges in deploying models on edge devices, TensorFlow Lite workflow, creating and converting TensorFlow or Keras models to TFLite, validating model performance, quantization, and compressing TFLite models. The teaching method includes tutorials and demonstrations. This course is intended for individuals interested in deploying machine learning models on edge devices.
Overview
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.
Taught by
freeCodeCamp.org
