Lecture 1 - Intro and Word Vectors
Lecture 2 - Word Vectors and Language Models
Lecture 3 - Backpropagation, Neural Network
Lecture 4 - Dependency Parsing
Lecture 5 - Recurrent Neural Networks
Lecture 6 - Sequence to Sequence Models
Lecture 7 - Attention, Final Projects and LLM Intro
Lecture 8 - Self-Attention and Transformers
Lecture 11 - Natural Language Generation
Lecture 10 - Post-training by Archit Sharma
Lecture 11 - Benchmarking by Yann Dubois
Lecture 12 - Neural Network by Shikhar Murty
Lecture 13 - Interfaces by Chaofei Fan
Lecture 14 - Reasoning and Agents by Shikhar Murty
Lecture 15 - After DPO by Nathan Lambert
Lecture 18 - NLP, Linguistics, Philosophy
Lecture 16 - Multimodal Deep Learning, Douwe Kiela
Model Interpretability & Editing, Been Kim
Python Tutorial, Manasi Sharma
PyTorch Tutorial, Drew Kaul
Hugging Face Tutorial, Eric Frankel
