PATCH EMBEDDING | Vision Transformers explained

ExplainingAI · Intermediate ·🧠 Large Language Models ·2y ago

About this lesson

I will cover Vision transformer in three parts. The first part which is this video focusses on patch embedding in vision transformer. I will go over all the details and explain everything happening inside the patch embedding in VIT in detail. I will also go over how an implementation of patch embedding for vision transformer in Pytorch would look like. The second part which goes through attention can be found here - Attention in Vision Transformer (Part Two) - https://www.youtube.com/watch?v=zT_el_cjiJw The third part which builds entire transformer and shows how to visualize attention maps and positional embeddings can be found below - Implementing Vision Transformer (Part Three) - https://www.youtube.com/watch?v=G6_IA5vKXRI *Timestamps* : 00:00 Intro 00:56 Need for Patch Embedding in Vision Transformer 01:30 Converting Image into Sequence of Patches 01:59 Patch Embedding Projection 02:45 Positional Information for Patches 03:40 CLS Token 04:10 Patch Embedding Responsibilities 04:40 Patch Embedding Module Implementation 08:02 Outro *Paper Link* - https://tinyurl.com/exai-vit-paper Implementation will be pushed here after all three videos are out - https://tinyurl.com/exai-vit-code *Subscribe* - https://tinyurl.com/exai-channel-link Background Track - Fruits of Life by Jimena Contreras Email - explainingai.official@gmail.com

Original Description

I will cover Vision transformer in three parts. The first part which is this video focusses on patch embedding in vision transformer. I will go over all the details and explain everything happening inside the patch embedding in VIT in detail. I will also go over how an implementation of patch embedding for vision transformer in Pytorch would look like. The second part which goes through attention can be found here - Attention in Vision Transformer (Part Two) - https://www.youtube.com/watch?v=zT_el_cjiJw The third part which builds entire transformer and shows how to visualize attention maps and positional embeddings can be found below - Implementing Vision Transformer (Part Three) - https://www.youtube.com/watch?v=G6_IA5vKXRI *Timestamps* : 00:00 Intro 00:56 Need for Patch Embedding in Vision Transformer 01:30 Converting Image into Sequence of Patches 01:59 Patch Embedding Projection 02:45 Positional Information for Patches 03:40 CLS Token 04:10 Patch Embedding Responsibilities 04:40 Patch Embedding Module Implementation 08:02 Outro *Paper Link* - https://tinyurl.com/exai-vit-paper Implementation will be pushed here after all three videos are out - https://tinyurl.com/exai-vit-code *Subscribe* - https://tinyurl.com/exai-channel-link Background Track - Fruits of Life by Jimena Contreras Email - explainingai.official@gmail.com
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related AI Lessons

Claude AI vs ChatGPT: Which One Is Actually Better in 2026?
Compare Claude AI and ChatGPT based on real-world usage and benchmarking to determine which one is better in 2026
Medium · AI
Claude AI vs ChatGPT: Which One Is Actually Better in 2026?
Compare Claude AI and ChatGPT to determine which AI model is better for your needs in 2026
Medium · Programming
IntelliBooks: Classic RAG vs Graph RAG vs Agentic RAG – Choosing the Right AI Retrieval Architecture for Enterprise AI
Learn to choose the right AI retrieval architecture for enterprise AI between Classic RAG, Graph RAG, and Agentic RAG
Dev.to AI
Fluid, natural voice translation with Gemini 3.5 Live Translate
Learn about Gemini 3.5 Live Translate, a new voice translation technology that enables fluid and natural conversations across languages
Dev.to AI

Chapters (9)

Intro
0:56 Need for Patch Embedding in Vision Transformer
1:30 Converting Image into Sequence of Patches
1:59 Patch Embedding Projection
2:45 Positional Information for Patches
3:40 CLS Token
4:10 Patch Embedding Responsibilities
4:40 Patch Embedding Module Implementation
8:02 Outro
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch →