1
0
Fork 0
Commit graph

46 commits

Author SHA1 Message Date
2ab445f9a8 Compute the test set error 2014-11-06 13:49:17 +01:00
78830aaea7 Validation curve function 2014-11-06 13:31:44 +01:00
3751214442 Use lambda=1 for regularizing the polynomial fit 2014-11-06 12:23:09 +01:00
717ea8c788 Polynomial feature mapping 2014-11-06 12:13:13 +01:00
1cc58802eb Learning curve function 2014-11-06 12:04:53 +01:00
90f2928cee Move .gitignore to top-level directory 2014-11-06 01:19:29 +01:00
2d6da3e3d4 Regularized linear regression gradient 2014-11-06 01:12:38 +01:00
6530916642 Regularized linear regression cost function 2014-11-06 00:53:49 +01:00
d93d111106 Add programming exercise 5 2014-11-05 11:20:50 +01:00
eccdcc0d81 Regularized NN gradient 2014-11-02 13:48:34 +01:00
bdecab8cf8 Implement back propagation 2014-11-02 13:27:11 +01:00
052f0625c3 Random initialization 2014-11-01 21:22:34 +01:00
863f1d7157 Compute the sigmoid gradient 2014-11-01 21:18:32 +01:00
395c5676dc Add regularization to the cost function 2014-11-01 20:42:58 +01:00
f2154a8cc1 Compute cost function for the neural network 2014-11-01 20:30:58 +01:00
be6f3cbdef Move PDFs in top directory 2014-11-01 14:54:47 +01:00
a17f47e396 Add programming exercise 4 2014-11-01 14:54:22 +01:00
073fbf0204 Add neural network prediction function 2014-10-23 23:36:15 +02:00
3bf3d9fdc3 Add prediction function for one-vs-all classification 2014-10-23 22:24:55 +02:00
cf0d25440c Train num_labels one-vs-all logistic regression classifiers 2014-10-23 21:17:20 +02:00
9117809537 Vectorized regularized logistic regression, again 2014-10-21 21:20:26 +02:00
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