Lecture 1: Transformer • Background on NLP and tasks • Tokenization • Embeddings • Word2vec, RNN, LSTM • Attention mechanism • Transformer architecture

Lecture 2: Transformer-based models & tricks • Attention approximation • MHA, MQA, GQA • Position embeddings (regular, learned) • RoPE and applications • Transformer-based architectures • BERT and its derivatives

Lecture 3: Large Language Models • Definition and architecture • Mixture of experts • Context length, temperature • Sampling strategies • Prompting, in-context learning • Chain of thought • Self-consistency

Lecture 4: LLM training • Pretraining • Quantization • Hardware optimization • Supervised finetuning (SFT) • Parameter-efficient finetuning (LoRA)

Lecture 5: LLM tuning • Preference tuning • RLHF overview • Reward modeling • RL approaches (PPO and variants) • DPO

Lecture 6: LLM reasoning • Reasoning models • RL for reasoning • GRPO • Scaling

Lecture 7: Agentic LLMs • Retrieval-augmented generation • Advanced RAG techniques • Function calling • Agents • ReAct framework

Lecture 8: LLM evaluation • LLM-as-a-judge overview • Best practices and benefits • Biases and pitfalls Lecture 81:49:25

Lecture 9: Current trends • Recap • Trending topics • Closing thoughts