Transformer Architecture Explained

Transformer Architecture Explanation from the paper: Attention is all you need. Watch each components of Transformer Architecture in Detail: 1) Tokenization    • LLM Training Starts Here: Dataset Preparat...   2) Embeddings    • What Are Word Embeddings?   3) Attention Mechanism    • How Attention Mechanism Works in Transform...   Read Original Paper Here: https://arxiv.org/abs/1706.03762 Timestamp: 0:00 - Introduction 1:15 - Dataset Preparation 2:15 - Encoder: Tokenization, Embedding, PE 5:50 - Encoder: Attention Mechanism 10:05 - Encoder: MHA, Add & Norm, FFNN 13:20 - Decoder: Tokenization, Embedding, PE, MMHA 16:27 - Decoder: Cross Attention, Output 18:05 - Transformer Inference

Join Today
Attention is all you need (Transformer) - Model explanation (including math), Inference and Training
▶︎

Attention is all you need (Transformer) - Model explanation (including math), Inference and Training

Transformers, the tech behind LLMs | Deep Learning Chapter 5
▶︎

Transformers, the tech behind LLMs | Deep Learning Chapter 5

Proposal-Free Open-Vocabulary 3D Instance Segmentation | SpaCeFormer
▶︎

Proposal-Free Open-Vocabulary 3D Instance Segmentation | SpaCeFormer

Self-Attention Explained: How Transformers Actually Work (Full Visual Breakdown)
▶︎

Self-Attention Explained: How Transformers Actually Work (Full Visual Breakdown)

Transformers Explained | Simple Explanation of Transformers
▶︎

Transformers Explained | Simple Explanation of Transformers

Multi-Head Attention Explained Visually | Simple Transformer Guide
▶︎

Multi-Head Attention Explained Visually | Simple Transformer Guide

Pretraining Large Language Models: Everything You Need to Know!
▶︎

Pretraining Large Language Models: Everything You Need to Know!

How Does the Transformer Encoder Actually Work? Complete Visual Breakdown
▶︎

How Does the Transformer Encoder Actually Work? Complete Visual Breakdown

KV Cache in LLM Inference - Complete Technical Deep Dive
▶︎

KV Cache in LLM Inference - Complete Technical Deep Dive

Positional embeddings in transformers EXPLAINED | Demystifying positional encodings.
▶︎

Positional embeddings in transformers EXPLAINED | Demystifying positional encodings.

Attention in transformers, step-by-step | Deep Learning Chapter 6
▶︎

Attention in transformers, step-by-step | Deep Learning Chapter 6

The KV Cache: Memory Usage in Transformers
▶︎

The KV Cache: Memory Usage in Transformers

Transformers Explained: The Discovery That Changed AI Forever
▶︎

Transformers Explained: The Discovery That Changed AI Forever

How DeepSeek Rewrote the Transformer [MLA]
▶︎

How DeepSeek Rewrote the Transformer [MLA]

Attention Is All You Need
▶︎

Attention Is All You Need

Transformers and Self-Attention (DL 19)
▶︎

Transformers and Self-Attention (DL 19)

Transformer models and BERT model: Overview
▶︎

Transformer models and BERT model: Overview

What Are Word Embeddings?
▶︎

What Are Word Embeddings?

Why Transformers Need Positional Encoding | Sin & Cos Explained Visually
▶︎

Why Transformers Need Positional Encoding | Sin & Cos Explained Visually

Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy
▶︎

Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy