Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Dive into the second part of a comprehensive lecture on quantization in machine learning, delivered by Prof. Song Han as part of MIT's 6.5940 course for Fall 2023. Explore advanced concepts such as Rooney Quantization, scaling factors, convolution techniques, and post-training quantization methods. Learn about quantization granularity, per-channel quantization, and the importance of clipping in the quantization process. Discover how to select optimal clipping ranges and fine-tune quantized models. Examine weight and activation quantization through practical examples, and gain insights into binary and ternary quantization techniques, including stochastic binarization. Access accompanying slides at efficientml.ai for a deeper understanding of these cutting-edge quantization strategies in efficient machine learning.