L-4 | Transformer Architecture — Foundations of Large Language Models

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 (English): @codewithaarohi 🔗 https://www.instagram.com/codewithaarohi/ 📧 You can also reach me at: aarohisingla1987@gmail.com 📸 Follow me on Instagram (Hindi): @codewithaarohihindi 🔗 https://instagram.com/codewithaarohihindi