Beginner Lessons

Lec 17. Generalization: Out-of-Distribution (OOD)

Lec 01. Introduction to Deep Learning

Lec 20. Scaling Laws

Lec 13. Representation Learning: Theory

Lec 10. Architectures: Memory

Lec 11. Representation Learning: Reconstruction-Based

Lec 14. Generative Models: Basics

Lec 03. Approximation Theory

Lec 12. Representation Learning: Similarity-Based

Lec 19. Transfer Learning: Data

Lec 07. Scaling Rules for Optimization

Lec 21. Language Models

Lec 08. Architectures: Transformers

Lec 06. Generalization Theory

PyTorch Tutorial

Lec 18. Transfer Learning: Models

Lecture 17: Redistribution and Welfare, Taxation Overview

Lecture 18: Overview of Taxation and Tax Base, Tax Incidence

Lecture 24: Corporate Taxation and Tax Reform

Lecture 01: Why Study Public Finance?

S7SE Special Episode: Collaborating with Community Colleges

8: Deep Learning for Natural Language – Transformers, Self-Supervised Learning

9: Generative AI – Large Language Models (LLMs) and Retrieval Augmented Generation (RAG)

5: Deep Learning for Natural Language – The Basics