Transformers Visually Explained

in this video, we build transformers from scratch and understand how they actually work — starting from embeddings and self-attention to multi-head attention, positional encoding, and the full encoder-decoder architecture , along with masking, cross-attention, and the difference between training and inference, so by the end you get a complete and intuitive understanding of how modern LLMs like GPT are built

Attention is all you need paper:- https://arxiv.org/abs/1706.03762

These are very important to know before you understand tranformers:-

Neural Networks:- https://youtu.be/sE6OaMndGZg

Backpropagation:- https://youtu.be/nAMkcgxKwfA

Normalization:- https://youtu.be/W2vqsTg-rDU

BatchNorm:-https://youtu.be/PaIKIXb3v9Q

RNNs:- https://youtu.be/eCwTQYcNG3o

Residual Connections:- https://youtu.be/M108HPERPc8

Link for the animation codes:- https://github.com/ByteQuest0/Animation_codes/tree/main/2026/Transfomers

00:00 Introduction – Why Transformers?
02:44 Tokenization and One-Hot Encoding
04:59 Word Embeddings Explained
08:37 Static Embeddings Problem (Bank Example)
15:12 Self-Attention
18:00 Why Scaling by √dk?
20:42 Self Attention Recap
21:33 Multihead Self Attention
25:29 Positional Encoding Intuition
30:45 Transformer Architecture Overview
31:36 Residual Connections + LayerNorm
33:00 Feed Forward Network Explained
33:25 Transformer Architecture Overview
34:00 Masked Multi-Head Attention
37:00 Cross Attention Explained
38:28 Transformer Architecture Overview
39:12 Stacked Layers (Nx)
39:37 Training vs Inference
42:43 Transformers Advantage

🎥 Animations created using Manim:
Manim is an open-source Python library for creating mathematical animations. Learn more or try it yourself:
🔗 https://www.manim.community

Let's Connect:-

GitHub:- https://github.com/ByteQuest0
Reddit:- https://www.reddit.com/r/ByteQuest/

Видео Transformers Visually Explained канала ByteQuest