Building LLMs from scratch (English)

   Monday, February 23, 2026   

Starting a new Lecture Notes Series on Building LLMs from scratch

Youtube Lecture Playlist Credits 

Channel Name: edureka!

So Let Us Start to This Journey of Learning 

Building LLMs from scratch By Lecture Notes together!

Lecture 1: Lecture 1: Building LLMs from scratch: Series introduction

Lecture 2: Lecture 2: Large Language Models (LLM) Basics

Lecture 3: Lecture 3: Pretraining LLMs vs Finetuning LLMs

Lecture 4: Lecture 4: What are transformers?

Lecture 5: Lecture 5: How does GPT-3 really work?

Lecture 6: Lecture 6: Stages of building an LLM from Scratch

Lecture 7: Lecture 7: Code an LLM Tokenizer from Scratch in Python

Lecture 8: Lecture 8: The GPT Tokenizer: Byte Pair Encoding

Lecture 9: Lecture 9: Creating Input-Target data pairs using Python DataLoader

Lecture 10: Lecture 10: What are token embeddings?

Lecture 11: Lecture 11: The importance of Positional Embeddings

Lecture 12: Lecture 12: The entire Data Preprocessing Pipeline of Large Language Models (LLMs)

Lecture 13: Lecture 13: Introduction to the Attention Mechanism in Large Language Models (LLMs)

Lecture 14: Lecture 14: Simplified Attention Mechanism - Coded from scratch in Python | No trainable weights

Lecture 15: Lecture 15: Coding the self attention mechanism with key, query and value matrices

Lecture 16: Lecture 16: Causal Self Attention Mechanism | Coded from scratch in Python

Lecture 17: Lecture 17: Multi Head Attention Part 1 - Basics and Python code

Lecture 18: Lecture 18: Multi Head Attention Part 2 - Entire mathematics explained

Lecture 19: Lecture 19: Birds Eye View of the LLM Architecture

Lecture 20: Lecture 20: Layer Normalization in the LLM Architecture

Lecture 21: GELU Activation Function in the LLM Architecture

Lecture 22: Shortcut connections in the LLM Architecture

Lecture 23: Coding the entire LLM Transformer Block

Lecture 24: Coding the 124 million parameter GPT-2 model

Lecture 25: Coding GPT-2 to predict the next token

Lecture 26: Measuring the LLM loss function

Lecture 27: Evaluating LLM performance on real dataset | Hands on project | Book data

Lecture 28: Coding the entire LLM Pre-training Loop

Lecture 29: Temperature Scaling in Large Language Models (LLMs)

Lecture 30: Top-k sampling in Large Language Models

Lecture 31: Saving and loading LLM model weights using PyTorch

Lecture 32: Loading pre-trained weights from OpenAI GPT-2

Lecture 33: Introduction to LLM Finetuning | Python Coding with hands-on-example

Lecture 34: Dataloaders in LLM Classification Finetuning | Python Coding | Hands on LLM project

Lecture 35: Coding the model architecture for LLM classification fine-tuning

Lecture 36: Coding a fine-tuned LLM spam classification model | From Scratch

Lecture 37: Introduction to LLM Instruction Fine-tuning | Loading Dataset | Alpaca Prompt format

Lecture 38: Data Batching in LLM instruction fine-tuning | Hands on project | Live Python coding

Lecture 39: Dataloaders in Instruction Fine-tuning

Lecture 40: Instruction fine-tuning: Loading pre-trained LLM weights

Lecture 41: LLM fine-tuning training loop | Coded from scratch

Lecture 42: Evaluating fine-tuned LLM using Ollama

Lecture 43: Build LLMs from scratch 20 minutes summary