Quantization explained with PyTorch - Post-Training Quantization, Quantization-Aware Training

In this video I will introduce and explain quantization: we will first start with a little introduction on numerical representation of integers and floating-point numbers in computers, then see what is quantization and how it works. I will explore topics like Asymmetric and Symmetric Quantization, Quantization Range, Quantization Granularity, Dynamic and Static Quantization, Post-Training Quantization and Quantization-Aware Training. Code: https://github.com/hkproj/quantization-notes PDF slides: https://github.com/hkproj/quantization-notes Chapters 00:00 - Introduction 01:10 - What is quantization? 03:42 - Integer representation 07:25 - Floating-point representation 09:16 - Quantization (details) 13:50 - Asymmetric vs Symmetric Quantization 15:38 - Asymmetric Quantization 18:34 - Symmetric Quantization 20:57 - Asymmetric vs Symmetric Quantization (Python Code) 24:16 - Dynamic Quantization & Calibration 27:57 - Multiply-Accumulate Block 30:05 - Range selection strategies 34:40 - Quantization granularity 35:49 - Post-Training Quantization 43:05 - Training-Aware Quantization