ECE 595ML Lecture 16.2: Preceptron - Properties of Preceptron Loss
ECE 595ML Lecture 16.2: Preceptron - Properties of Preceptron Loss
-
1. Lecture 16.2: Perceptron - Pro…
0
00:00/00:00
-
2. Outline
8.7420754087420764
00:00/00:00
-
3. Convexity of Perceptron (Soft)…
26.826826826826828
00:00/00:00
-
4. Convexity of Perceptron (Soft)…
227.52752752752752
00:00/00:00
-
5. Convexity of Perceptron (Soft)…
318.81881881881884
00:00/00:00
-
6. Implication of Convexity
443.64364364364366
00:00/00:00
-
7. Comparing Perceptron and Bayes…
710.71071071071071
00:00/00:00
-
8. Comparing Perceptron and Bayes…
745.37871204537873
00:00/00:00
-
9. Comparing Perceptron and Bayes…
816.5165165165165
00:00/00:00
-
10. Comparing Perceptron and Bayes…
969.36936936936945
00:00/00:00
-
11. Comparing Perceptron and Bayes…
991.59159159159162
00:00/00:00
-
12. Perceptron with Hard Loss
1077.6776776776778
00:00/00:00
-
13. Re-defining the Loss
1165.3653653653655
00:00/00:00
-
14. Perceptron Algorithm
1171.8718718718719
00:00/00:00
-
15. Updating One Sample
1176.0427093760427
00:00/00:00
-
16. Updating One Sample
1231.6316316316318
00:00/00:00
-
17. Updating One Sample
1270.5038371705039
00:00/00:00
-
18. Updating One Sample
1279.0790790790791
00:00/00:00
-
19. Updating One Sample
1335.5021688355023
00:00/00:00
-
20. Reading List
1337.4708041374709
00:00/00:00