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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
Chapters
1
Introduction
0:00
2
Learning Paradigms
0:34
3
Optimization & Gradient Descent
1:09
4
The Perceptron
1:43
5
Activation Functions
2:17
6
Deep Architecture
2:51
7
The Learning Cycle
3:27
8
Convolutional Neural Networks
4:00
9
The Convolution Operation
4:32
10
Stride and Padding
5:05
11
Pooling Layers
5:33
12
Decision Trees
6:03
13
Entropy and Information Gain
6:35
14
Ensemble Strategies
7:07
15
K-Means Clustering
7:41
16
DBSCAN Density Logic
8:12
17
Model Validation & Tuning
8:45
18
Conclusion
9:17
