Quantization in Neural Networks - Part II - Lecture 6
Offered By: MIT HAN Lab via YouTube
Course Description
Overview
Explore post-training quantization and quantization-aware training techniques for neural network quantization in this comprehensive lecture from MIT's 6.S965 course. Delve into advanced topics such as binary/ternary quantization and mixed-precision quantization. Gain insights into efficient machine learning techniques that enable powerful deep learning applications on resource-constrained devices. Learn how to overcome challenges in deploying neural networks on mobile and IoT devices, and discover methods to accelerate neural network training. Access accompanying slides and additional course materials to enhance your understanding of efficient deep learning computing and TinyML.
Syllabus
Lecture 06 - Quantization (Part II) | MIT 6.S965
Taught by
MIT HAN Lab
Related Courses
Deploying TinyMLHarvard University via edX Learning TinyML
LinkedIn Learning Create and Connect Secure and Trustworthy IoT Devices
Microsoft via YouTube Speech-to-Intent on MCU: TinyML for Efficient Device Control - Lecture 6
Hardware.ai via YouTube Wio Terminal TinyML Course - People Counting and Azure IoT Central Integration - Part 3
Hardware.ai via YouTube