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Author	SHA1	Message	Date
neingeist	f8c0087ff3	Find closest centroids	2014-11-17 23:01:44 +01:00
neingeist	229023b69c	Add exercise 7	2014-11-17 21:58:10 +01:00
neingeist	348d6325cb	Email feature extraction	2014-11-13 23:50:52 +01:00
neingeist	f0d4b4d208	Preprocess email	2014-11-13 23:47:04 +01:00
neingeist	203cbc997c	Implement grid search and determine best parameters for C and sigma	2014-11-13 23:34:04 +01:00
neingeist	e67166bc8e	Implement Gaussian kernel	2014-11-13 22:59:48 +01:00
neingeist	7ab47a4d35	Add exercise 6	2014-11-10 23:44:39 +01:00
neingeist	2ab445f9a8	Compute the test set error	2014-11-06 13:49:17 +01:00
neingeist	78830aaea7	Validation curve function	2014-11-06 13:31:44 +01:00
neingeist	3751214442	Use lambda=1 for regularizing the polynomial fit	2014-11-06 12:23:09 +01:00
neingeist	717ea8c788	Polynomial feature mapping	2014-11-06 12:13:13 +01:00
neingeist	1cc58802eb	Learning curve function	2014-11-06 12:04:53 +01:00
neingeist	90f2928cee	Move .gitignore to top-level directory	2014-11-06 01:19:29 +01:00
neingeist	2d6da3e3d4	Regularized linear regression gradient	2014-11-06 01:12:38 +01:00
neingeist	6530916642	Regularized linear regression cost function	2014-11-06 00:53:49 +01:00
neingeist	d93d111106	Add programming exercise 5	2014-11-05 11:20:50 +01:00
neingeist	eccdcc0d81	Regularized NN gradient	2014-11-02 13:48:34 +01:00
neingeist	bdecab8cf8	Implement back propagation	2014-11-02 13:27:11 +01:00
neingeist	052f0625c3	Random initialization	2014-11-01 21:22:34 +01:00
neingeist	863f1d7157	Compute the sigmoid gradient	2014-11-01 21:18:32 +01:00
neingeist	395c5676dc	Add regularization to the cost function	2014-11-01 20:42:58 +01:00
neingeist	f2154a8cc1	Compute cost function for the neural network	2014-11-01 20:30:58 +01:00
neingeist	be6f3cbdef	Move PDFs in top directory	2014-11-01 14:54:47 +01:00
neingeist	a17f47e396	Add programming exercise 4	2014-11-01 14:54:22 +01:00
neingeist	073fbf0204	Add neural network prediction function	2014-10-23 23:36:15 +02:00
neingeist	3bf3d9fdc3	Add prediction function for one-vs-all classification	2014-10-23 22:24:55 +02:00
neingeist	cf0d25440c	Train num_labels one-vs-all logistic regression classifiers	2014-10-23 21:17:20 +02:00
neingeist	9117809537	Vectorized regularized logistic regression, again	2014-10-21 21:20:26 +02:00
neingeist	326a924044	Add exercise 3	2014-10-21 20:59:55 +02:00
neingeist	f9243ef593	Simplify regularization term	2014-10-16 01:03:58 +02:00
neingeist	9e9b9990bb	Compute the gradient for regularized logistic regression	2014-10-15 19:54:07 +02:00
neingeist	f391ac661e	Add cost function for regularized logistic regression	2014-10-15 10:10:30 +02:00
neingeist	224a17e4d3	Plot negative samples in red	2014-10-14 10:19:09 +02:00
neingeist	c0b4d95f75	Predict	2014-10-14 10:14:29 +02:00
neingeist	31c4ac1967	Compute the gradient for logistic regression	2014-10-14 07:46:10 +02:00
neingeist	8c100a3f49	Compute the cost function for logistic regression	2014-10-14 07:40:42 +02:00
neingeist	b863a3863e	.gitignore ml_login_data.mat	2014-10-13 23:15:15 +02:00
neingeist	efe94282b4	Compute the sigmoid function	2014-10-13 23:14:44 +02:00
neingeist	d52fa95328	Plot the data	2014-10-13 23:03:23 +02:00
neingeist	580e52ddc5	Initial commit of ex2	2014-10-13 22:56:53 +02:00
neingeist	ba377e29ba	Linear regressing using the normal equation	2014-10-02 22:48:53 +02:00
neingeist	ad9ab582de	Gradient descent for multiple features	2014-10-02 22:33:18 +02:00
neingeist	3dc8897634	Compute cost for multiple variables	2014-10-02 22:32:56 +02:00
neingeist	e38033c00d	Clean up whitespace	2014-10-02 22:32:37 +02:00
neingeist	8559c243c5	Normalize features	2014-10-02 22:20:44 +02:00
neingeist	613220bb3e	Clean up whitespace	2014-10-02 22:20:31 +02:00
neingeist	38064db8b3	Print out the cost function J	2014-10-02 22:08:08 +02:00
neingeist	2bf076c2fc	Gradient descent for one variable	2014-10-02 21:32:24 +02:00
neingeist	514431e135	.gitignore login data	2014-10-01 22:06:15 +02:00
neingeist	14c85aa85c	Compute Cost (for one variable)	2014-10-01 22:05:13 +02:00