Building LLMs from scratch - Coding the 124 million parameter GPT-2 model

In this lecture, we code the entire 124 million parameter GPT-2 Model class in Python. This includes the following components: Token + Positional embedding Transformer block Layer normalization Output layer We understand the theory, mathematical intuition and also do the coding for the entire implementation. After this lecture, you will have a firm understanding of how the entire GPT-2 architecture works. The key reference book which this video series very closely follows is Build a Large Language Model from Scratch by Manning Publications. All schematics and their descriptions are borrowed from this incredible book! This book serves as a comprehensive guide to understanding and building large language models, covering key concepts, techniques, and implementations. Affiliate links for purchasing the book will be added soon. Stay tuned for updates! 0:00 Birds eye view of GPT-2 architecture 7:45 Token, positional and input embeddings 17:29 Dropout layer 20:47 The 8 steps of the transformer block 32:37 Post transformer layer normalisation 33:36 Output layer 40:20 Coding the entire GPT-2 architecture in Python 51:42 Testing the GPT model class on a simple example 53:51 Parameter and memory calculations 57:56 Conclusion and summary Link to code file: https://drive.google.com/file/d/1k4TwMW6HHDiS1tcbGO5ip3zD3BonwSa0/view?usp=sharing ================================================= ✉️ Join our FREE Newsletter: https://vizuara.ai/our-newsletter/ ================================================= Vizuara philosophy: As we learn AI/ML/DL the material, we will share thoughts on what is actually useful in industry and what has become irrelevant. We will also share a lot of information on which subject contains open areas of research. Interested students can also start their research journey there. Students who are confused or stuck in their ML journey, maybe courses and offline videos are not inspiring enough. What might inspire you is if you see someone else learning and implementing machine learning from scratch. No cost. No hidden charges. Pure old school teaching and learning. ================================================= 🌟 Meet Our Team: 🌟 🎓 Dr. Raj Dandekar (MIT PhD, IIT Madras department topper) 🔗 LinkedIn: https://www.linkedin.com/in/raj-abhijit-dandekar-67a33118a/ 🎓 Dr. Rajat Dandekar (Purdue PhD, IIT Madras department gold medalist) 🔗 LinkedIn: https://www.linkedin.com/in/rajat-dandekar-901324b1/ 🎓 Dr. Sreedath Panat (MIT PhD, IIT Madras department gold medalist) 🔗 LinkedIn: https://www.linkedin.com/in/sreedath-panat-8a03b69a/ 🎓 Sahil Pocker (Machine Learning Engineer at Vizuara) 🔗 LinkedIn: https://www.linkedin.com/in/sahil-p-a7a30a8b/ 🎓 Abhijeet Singh (Software Developer at Vizuara, GSOC 24, SOB 23) 🔗 LinkedIn: https://www.linkedin.com/in/abhijeet-singh-9a1881192/ 🎓 Sourav Jana (Software Developer at Vizuara) 🔗 LinkedIn: https://www.linkedin.com/in/souravjana131/