Lecture 1: Building LLMs from scratch: Series introduction Lecture 2: Large Language Models (LLM) Basics Lecture 3: Pretraining LLMs vs Finetuning LLMs Lecture 4: What are transformers? Lecture 5: How does GPT-3 really work? Lecture 6: Stages of building an LLM from Scratch Lecture 7: Code an LLM Tokenizer from Scratch in Python Lecture 8: The GPT Tokenizer: Byte Pair Encoding Lecture 9: Creating Input-Target data pairs using Python DataLoader Lecture 10: What are token embeddings? Lecture 11: The importance of Positional Embeddings Lecture 12: The entire Data Preprocessing Pipeline of Large Language Models (LLMs) Lecture 13: Introduction to the Attention Mechanism in Large Language Models (LLMs) Lecture 14: Simplified Attention Mechanism - Coded from scratch in Python | No trainable weights Lecture 15: Coding the self attention mechanism with key, query and value matrices Lecture 16: Causal Self Attention Mechanism | Coded from scratch in Python Lecture 17: Multi Head Attention Part 1 - Basics and Python code Lecture 18: Multi Head Attention Part 2 - Entire mathematics explained Lecture 19: Birds Eye View of the LLM Architecture Lecture 20: Layer Normalization in the LLM Architecture GELU Activation Function in the LLM Architecture Shortcut connections in the LLM Architecture Coding the entire LLM Transformer Block Coding the 124 million parameter GPT-2 model Coding GPT-2 to predict the next token Measuring the LLM loss function Evaluating LLM performance on real dataset | Hands on project | Book data Coding the entire LLM Pre-training Loop Temperature Scaling in Large Language Models (LLMs) Top-k sampling in Large Language Models Saving and loading LLM model weights using PyTorch Loading pre-trained weights from OpenAI GPT-2 Introduction to LLM Finetuning | Python Coding with hands-on-example Dataloaders in LLM Classification Finetuning | Python Coding | Hands on LLM project Coding the model architecture for LLM classification fine-tuning Coding a fine-tuned LLM spam classification model | From Scratch Introduction to LLM Instruction Fine-tuning | Loading Dataset | Alpaca Prompt format Data Batching in LLM instruction fine-tuning | Hands on project | Live Python coding Dataloaders in Instruction Fine-tuning Instruction fine-tuning: Loading pre-trained LLM weights LLM fine-tuning training loop | Coded from scratch Evaluating fine-tuned LLM using Ollama Build LLMs from scratch 20 minutes summary