RAG & Vector Search

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1. Introduction, Finite Automata, Regular Expressions

HNSW for Vector Search Explained and Implemented with Faiss (Python)

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Best Indexes for Similarity Search in Faiss

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Lecture 1: Introduction and Overview I (14.13 Psychology and Economics, Spring 2020)

Lecture 5: Time Preferences (Applications) I

Lecture 7: Risk Preferences I

Lecture 9: Reference-Dependent Preferences

Lecture 3: Time Preferences (Theory) I

Lecture 4: Time Preferences (Theory) II

Lecture 10: Social Preferences I

Lecture 21: Poverty through the Lens of Psychology

Lecture 15: Utility from Beliefs; Learning I

Lecture 2: Introduction and Overview II

Lecture 16: Utility from Beliefs; Learning II

Lecture 18: Gender, Discrimination, and Identity

Lecture 6: Time Preferences (Applications) II

Lecture 13: Social Preferences IV

Lecture 8: Risk Preferences II

Lecture 19: Defaults, Nudges, and Frames

Lecture 11: Social Preferences II

Lecture 22: Happiness and Mental Health

Lecture 12: Social Preferences III

Composite Indexes and the Faiss Index Factory

1. Algorithms and Computation

2. Data Structures and Dynamic Arrays

7. Binary Trees, Part 2: AVL

8. Binary Heaps

11. Weighted Shortest Paths

19. Complexity

16. Dynamic Programming, Part 2: LCS, LIS, Coins

Problem Session 5

9. Breadth-First Search

3. Sets and Sorting

5. Linear Sorting

6. Binary Trees, Part 1