This document provides an overview of deep learning on mobile devices. It discusses why deep learning is important for mobile, including issues like privacy, reliability and latency. It then covers topics like how to train models for mobile using techniques like transfer learning and fine-tuning. The document also discusses frameworks for running models efficiently on mobile like Core ML, TensorFlow Lite and Google's ML Kit. It explores how hardware impacts performance and how to optimize models. Finally, it touches on applications of deep learning on mobile and techniques like federated learning.
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