Все публикации

Lecture 15, Part0:27:12

Lecture 15, Part 3, Axiomatic Explanations

Lecture 15, Part0:09:18

Lecture 15, Part 4, Heuristics

Lecture 15, Part0:15:32

Lecture 15, Part 2, Local Models and Conditioning

Lecture 15, Part0:26:05

Lecture 15, Part 1, Simple Explanations

Lecture 14, Part0:31:22

Lecture 14, Part 3, Fairness in Practice

Lecture 14, Part0:29:44

Lecture 14, Part 2, Fairness and Scores

Lecture 14, Part0:25:33

Lecture 14, Part 1, Fairness Background

Lecture 8, Part0:31:48

Lecture 8, Part 4, Graphs and Networks

Lecture 8, Part0:15:57

Lecture 8, Part 3 More about Sequence Models

Lecture 8, Part0:22:22

Lecture 8, Part 2 Sequence Models

Lecture 8, Part0:24:44

Lecture 8, Part 1 Dependent Random Variables

Lecture 6, Part0:27:11

Lecture 6, Part 2, Covariate Shift

Lecture 6, Part0:25:17

Lecture 6, Part 4, Adversarial Data

Lecture 6, Part0:21:45

Lecture 6, Part 3, More Math for Covariate Shift

Lecture 6, Part0:14:36

Lecture 6, Part 1, Generalization

Lecture 7, Part0:26:53

Lecture 7, Part 2 Label Shift

Lecture 7, Part0:40:03

Lecture 7, Part 1 - Two-sample tests

5.3 BERT and0:15:56

5.3 BERT and Applications

5.2 Transformers0:14:00

5.2 Transformers

5.1 Attention0:10:20

5.1 Attention

4.3 Deep and0:10:21

4.3 Deep and Bidirectional LSTMs

2.4 Autograd in0:11:41

2.4 Autograd in PyTorch

4.2 LSTM and0:23:41

4.2 LSTM and Friends

2.3 Linear Algebra0:23:12

2.3 Linear Algebra