Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Introduction
00:00 • 2min
Using Distance and Similarity Metrics in Machine Learning
02:08 • 3min
Cartesian Distance Slash Similarity Metrics
04:38 • 3min
Norms, Normed Distances, Euclid Similarity
07:09 • 3min
Manhattan Distance Formula, Absolute Value of X I Minus Y I
10:27 • 2min
The Manhattan Distance
12:08 • 2min
Euclidian Distance vs Manhattan Distance
14:28 • 2min
The Loss Function of a Regression Machine Learning Model
16:55 • 3min
Machine Learning - Is There a Difference Between Mean Absolute Error and Mean Squared Air?
19:57 • 2min
The Difference Between Euclidean Distance and Dot Product Functions
22:27 • 4min
The Dot Product in Machine Learning
26:04 • 3min
T F, I, D F Vectorizers in Cartesian Space
29:21 • 4min
Cosin Distances in Natural Language Processing
33:17 • 2min
The Cosin Distance in Machine Learning
34:57 • 2min
How to Cluster Word Vectors and Document Vectors
37:03 • 5min