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On-Device Training and Transfer Learning - Lecture 15

MIT HAN Lab via YouTube

Overview

Explore on-device training and transfer learning techniques in this lecture from MIT's course on TinyML and Efficient Deep Learning Computing. Dive into the memory bottleneck challenges of on-device training and discover efficient algorithms like TinyTL for on-device transfer learning. Examine system support for efficient on-device training and gain insights into implementing deep learning applications on resource-constrained devices. Learn from instructor Song Han as he covers topics such as model compression, pruning, quantization, neural architecture search, and distillation. Acquire hands-on experience in deploying neural networks on mobile devices, IoT devices, and microcontrollers through practical examples and an open-ended design project focused on mobile AI.

Syllabus

Lecture 15 - On-Device Training and Transfer Learning (Part I) | MIT 6.S965

Taught by

MIT HAN Lab

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