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

Umar Jamil · Beginner ·📐 ML Fundamentals ·2y ago
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 quant…
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Chapters (15)

Introduction
1:10 What is quantization?
3:42 Integer representation
7:25 Floating-point representation
9:16 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
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