L-4 | Transformers Explained: The Architecture Behind All Modern LLMs
In this lecture, we deep dive into the Transformer architecture, the foundation behind all modern Large Language Models (LLMs) like GPT, LLaMA, Mistral, and BERT. In previous classes, we built an LLM from scratch. In this video, we finally explain the architecture powering those models. 📌 What you’ll learn in this video: ✔ What the original Transformer architecture (2017) looks like ✔ Why modern LLMs do NOT use the full encoder–decoder Transformer ✔ How decoder-only Transformers power GPT-1, GPT-2, GPT-3, and LLaMA ✔ Tokenization → Embedding Layer → Backpropagation (intuitive explanation) ✔ How embedding matrices are learned during training ✔ Why vocabulary size and d_model matter ✔ How gradients update embedding weights 📚 Papers discussed: Attention Is All You Need (2017) Improving Language Understanding by Generative Pre-Training (GPT-1) Language Models are Unsupervised Multitask Learners (GPT-2) Language Models are Few-Shot Learners (GPT-3) If you want to build your own LLM from scratch, understanding the Transformer architecture is absolutely essential. 👉 Like, Comment, Share & Subscribe — your support really motivates me to create in-depth ML & AI content ❤️ 📸 Follow me on Instagram: @codewithaarohi 🔗 / codewithaarohi 📧 You can also reach me at: [email protected]
L-5 | Positional Encoding in Transformers Explained
L-2 | Build a Mini GPT Model From Scratch Using PyTorch | Step-by-Step Tutorial for Beginners
Attention in transformers, step-by-step | Deep Learning Chapter 6
L-6 | Transformer Encoder Explained | Self-Attention, Q K V
L-3 | LLM Tokenizers Explained: BPE, SentencePiece, Pretrained vs Custom (Full Hands-On Guide)
Transformers Explained | Simple Explanation of Transformers
L-1 | Understanding LLMs — Conceptually & Mathematically | Lecture 1 | LLMs Course
Query, Key and Value Matrix for Attention Mechanisms in Large Language Models
Transformer Architecture Explained
Why is This the Scariest Chart in Electrical Engineering?
Transformers, the tech behind LLMs | Deep Learning Chapter 5
China quietly saved the world last month
BERT Demystified: Like I’m Explaining It to My Younger Self
Transformers, explained: Understand the model behind GPT, BERT, and T5
L-2 | Let’s Build a GPT-Style Language Model Step by Step (Using PyTorch)
L-4 | Transformer Architecture — Foundations of Large Language Models
From Child Prodigy to Winning Fields Medal, Nobel of Math
Transformer Architecture Explained 'Attention Is All You Need'
