Building LLMs from scratch - Lecture 9: Creating Input-Target data pairs using Python DataLoader

In this lecture, we will learn about creating input output pairs required for LLM training. To do this, we dive deeper into Dataset and DataLoaders in Python. We also learn about processing data in batches, parallel computing and the effect of stride in creating input-output batches. 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 Introduction 03:01 What are input-target pairs in LLMs? 13:30 Coding input-output pairs in Python 16:46 What is context size? 22:50 Creating a DataLoader 31:06 Implementing DataLoader in Python 43:04 Batch size and parallel computing 45:40 What is stride? 49:42 Effect of larger batch size 51:28 Lecture recap Datasets and DataLoaders in Python: (1) https://pytorch.org/tutorials/beginner/basics/data_tutorial.html (2) https://pytorch.org/docs/stable/data.html Entire Code file link: https://drive.google.com/file/d/1srG6CyM0dWArPVAumNIJCFDskFrC1COn/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/