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Author SHA1 Message Date
326a924044 Add exercise 3 2014-10-21 20:59:55 +02:00
f9243ef593 Simplify regularization term 2014-10-16 01:03:58 +02:00
9e9b9990bb Compute the gradient for regularized logistic regression 2014-10-15 19:54:07 +02:00
f391ac661e Add cost function for regularized logistic regression 2014-10-15 10:10:30 +02:00
224a17e4d3 Plot negative samples in red 2014-10-14 10:19:09 +02:00
c0b4d95f75 Predict 2014-10-14 10:14:29 +02:00
31c4ac1967 Compute the gradient for logistic regression 2014-10-14 07:46:10 +02:00
8c100a3f49 Compute the cost function for logistic regression 2014-10-14 07:40:42 +02:00
b863a3863e .gitignore ml_login_data.mat 2014-10-13 23:15:15 +02:00
efe94282b4 Compute the sigmoid function 2014-10-13 23:14:44 +02:00
d52fa95328 Plot the data 2014-10-13 23:03:23 +02:00
580e52ddc5 Initial commit of ex2 2014-10-13 22:56:53 +02:00
ba377e29ba Linear regressing using the normal equation 2014-10-02 22:48:53 +02:00
ad9ab582de Gradient descent for multiple features 2014-10-02 22:33:18 +02:00
3dc8897634 Compute cost for multiple variables 2014-10-02 22:32:56 +02:00
e38033c00d Clean up whitespace 2014-10-02 22:32:37 +02:00
8559c243c5 Normalize features 2014-10-02 22:20:44 +02:00
613220bb3e Clean up whitespace 2014-10-02 22:20:31 +02:00
38064db8b3 Print out the cost function J 2014-10-02 22:08:08 +02:00
2bf076c2fc Gradient descent for one variable 2014-10-02 21:32:24 +02:00
514431e135 .gitignore login data 2014-10-01 22:06:15 +02:00
14c85aa85c Compute Cost (for one variable) 2014-10-01 22:05:13 +02:00
bc0eb3519f Plot the data 2014-09-30 22:25:32 +02:00
136815f0b7 Warmup exercise 2014-09-30 22:25:18 +02:00
79825e97f4 import exercise 1: IV. Linear Regression with Multiple Variables (Week 2) 2014-09-30 22:24:33 +02:00