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Deep Learning & Machine Learning Fundamentals
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Deep Learning & Machine Learning Fundamentals
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0:00 / 9:48
Chapters
Summary
Transcript
Introduction
0:00 - 0:33
Learning Paradigms
0:34 - 1:08
Optimization & Gradient Descent
1:09 - 1:42
The Perceptron
1:43 - 2:16
Activation Functions
2:17 - 2:50
Deep Architecture
2:51 - 3:26
The Learning Cycle
3:27 - 3:59
Convolutional Neural Networks
4:00 - 4:31
The Convolution Operation
4:32 - 5:04
Stride and Padding
5:05 - 5:32
Pooling Layers
5:33 - 6:02
Decision Trees
6:03 - 6:34
Entropy and Information Gain
6:35 - 7:06
Ensemble Strategies
7:07 - 7:40
K-Means Clustering
7:41 - 8:11
DBSCAN Density Logic
8:12 - 8:44
Model Validation & Tuning
8:45 - 9:16
Conclusion
9:17 - 9:48