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Sotheara Kang
  • 12/22/2014

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Learning

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4.6 Machine Learning Normal Equation
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14:14
1.4 Machine Learning Unsupervised Learning
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12/22/2014
8:10
2.1 Machine Learning Model Representation
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12/22/2014
12:29
1.3 Machine Learning Supervised Learning
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12/22/2014
7:14
1.2 Machine Learning What is Machine Learning
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12/22/2014
6:55
1.1 Machine Learning Welcome
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12/22/2014
8:12
2.2 Machine Learning Cost Function
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12/22/2014
11:09
2.3 Machine Learning Cons Function - Intuition I
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12/22/2014
11:30
2.5 Machine Learning Gradient Descent
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12/22/2014
10:21
2.7 Machine Learning Gradient Descent For Linear Regression
Sotheara Kang
12/22/2014
8:45
3.1 Machine Learning Matrices and Vectors
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12/22/2014
11:09
3.4 Machine Learning Matrix Matrix Multiplication
Sotheara Kang
12/22/2014
5:50
2.8 Machine Learning What's Next
Sotheara Kang
12/22/2014
9:02
3.5 Machine Learning Matrix Multiplication Properties
Sotheara Kang
12/22/2014
11:51
2.6 Machine Learning Gradient Descent Intuition
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12/22/2014
13:39
3.3 Machine Learning Matrix Vector Multiplication
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12/22/2014
6:54
3.2 Machine Learning Addition and Scalar Multiplication
Sotheara Kang
12/22/2014
8:48
2.4 Machine Learning Cost FUnction - Intuition II
Sotheara Kang
12/22/2014
8:58
4.4 Machine Learning Gradient Descent in Practice II - Learning Rate
Sotheara Kang
12/22/2014
7:39
4.5 Machine Learning Features and Polynomial Regression
Sotheara Kang
12/22/2014
5:05
4.2 Machine Learning Gradient Descent for Multiple Variable
Sotheara Kang
12/22/2014
13:59
5.1 Machine Learning Basic Operations
Sotheara Kang
12/22/2014
11:13
3.6 Machine Learning Inverse and Transpose
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12/22/2014
5:59
4.7 Machine Learning Normail Equation Noninvertibility
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12/22/2014
9:38
5.4 Machine Learning Plotting Data
Sotheara Kang
12/22/2014