635: The Perils of Manually Labeling Data for Machine Learning Models

1

Introduction

00:00 • 2min

2

The Coldest Winter of My Life Was the Summer in San Francisco

02:09 • 2min

3

Is Bias Good in Data Science?

03:41 • 2min

4

Is There a Bias Parameter?

05:26 • 4min

5

The Arguments Against Handling Labeling

09:46 • 3min

6

The Final Cost of Hand Labeling Machines

12:17 • 5min

7

Machine Learning - Is Iterative a Good Partner?

16:50 • 4min

8

Is There a Bottleneck in the Development of AI?

20:56 • 2min

9

Watchful

22:40 • 5min

10

The Mathematical Foundations of Machine Learning Course

27:58 • 4min

11

Watchful

31:54 • 5min

12

Do You Have Degenerative Bias?

36:32 • 2min

13

What Is Weekly Supervised Learning?

38:22 • 5min

14

Is It Ground Truth or Gold Data?

43:19 • 3min

15

The Ground Truth of What I Clicked on in My Social Media Feed

45:56 • 5min

16

Using Probabilistic Labels in Computer Science

50:42 • 3min

17

How to Create Labeling Functions in Python

53:45 • 4min

18

Why Not Just Use Elasticsearch?

58:00 • 5min

19

Using Co-Pilots, You Don't Need a Human to Be Sitting There

01:03:12 • 2min

20

The Simulation Engine Is Intelligent

01:04:47 • 3min

21

Are You Hiring Data Scientists?

01:07:26 • 2min

22

Is There a Tool of Thought for You?

01:09:44 • 3min

23

Emacs VIM Key Binding Integration

01:12:38 • 2min

24

The Three Body Problem Series by Sichin Liu

01:14:09 • 4min