The Most Underrated Layer Inside Every AI Model

Jia-Bin Huang ยท Beginner ยท๐Ÿ“„ Research Papers Explained ยท2mo ago

About this lesson

Why does every AI model use normalization? ๐Ÿค” Normalization layers are hidden inside every transformer model. In this video, we explore: - Why does AI training become unstable without normalization? - What do normalization layers actually do? - How simple alternatives can do just as well as normalization layers. Chapters: 00:00 Introduction 00:35 Gradient updates without normalization 02:45 Types of normalization layers 03:28 Visualization of normalization layers 05:10 S-shaped input-output mapping 06:46 Dynamic Tanh (DyT) layer 07:44 Comparison: DyT versus LayerNorm 09:41 Why DyT works? 10:21 Implementing DyT layer 11:06 Searching for a stronger function 12:07 Comparison: Derf versus DyT and LayerNorm References: [Layer Norm] https://arxiv.org/abs/1607.06450 [RMS Norm] https://proceedings.neurips.cc/paper/2019/hash/1e8a19426224ca89e83cef47f1e7f53b-Abstract.html [Batch Norm] https://arxiv.org/abs/1502.03167 [Instance Norm] https://arxiv.org/abs/1607.08022 [DyT] https://arxiv.org/abs/2503.10622 [Derf] https://arxiv.org/abs/2512.10938 Video made with Manim: https://www.manim.community/

Original Description

Why does every AI model use normalization? ๐Ÿค” Normalization layers are hidden inside every transformer model. In this video, we explore: - Why does AI training become unstable without normalization? - What do normalization layers actually do? - How simple alternatives can do just as well as normalization layers. Chapters: 00:00 Introduction 00:35 Gradient updates without normalization 02:45 Types of normalization layers 03:28 Visualization of normalization layers 05:10 S-shaped input-output mapping 06:46 Dynamic Tanh (DyT) layer 07:44 Comparison: DyT versus LayerNorm 09:41 Why DyT works? 10:21 Implementing DyT layer 11:06 Searching for a stronger function 12:07 Comparison: Derf versus DyT and LayerNorm References: [Layer Norm] https://arxiv.org/abs/1607.06450 [RMS Norm] https://proceedings.neurips.cc/paper/2019/hash/1e8a19426224ca89e83cef47f1e7f53b-Abstract.html [Batch Norm] https://arxiv.org/abs/1502.03167 [Instance Norm] https://arxiv.org/abs/1607.08022 [DyT] https://arxiv.org/abs/2503.10622 [Derf] https://arxiv.org/abs/2512.10938 Video made with Manim: https://www.manim.community/
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Chapters (11)

Introduction
0:35 Gradient updates without normalization
2:45 Types of normalization layers
3:28 Visualization of normalization layers
5:10 S-shaped input-output mapping
6:46 Dynamic Tanh (DyT) layer
7:44 Comparison: DyT versus LayerNorm
9:41 Why DyT works?
10:21 Implementing DyT layer
11:06 Searching for a stronger function
12:07 Comparison: Derf versus DyT and LayerNorm
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