coursera-ml-007-exercises

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eb1df682eb 📝 README.md master neingeist 2019-12-21 16:55:13 +0100
10ef3bd80e Regularized Gradient neingeist 2014-11-27 23:50:42 +0100
111a0d1cca Regularized Cost neingeist 2014-11-27 23:47:18 +0100
e1e66778f8 Collaborative Filtering Gradient neingeist 2014-11-27 23:43:53 +0100
fc28204594 Collaborative Filtering Cost neingeist 2014-11-27 23:31:49 +0100
87457bb7b4 Select threshold neingeist 2014-11-26 22:46:38 +0100
4bc9a2b246 Estimate Gaussian neingeist 2014-11-26 02:15:27 +0100
33e8cbac01 Add exercise 8 neingeist 2014-11-26 00:20:22 +0100
beb652a5be Recover data neingeist 2014-11-19 21:22:28 +0100
798f82ecc1 Project data neingeist 2014-11-19 21:12:54 +0100
2b98bd80f0 Implement PCA neingeist 2014-11-19 20:47:27 +0100
5f3f65c69c Random initialization neingeist 2014-11-17 23:20:44 +0100
6c51a29ca2 Compute centroid means (vectorized) neingeist 2014-11-17 23:13:57 +0100
39b09f144a Compute centroid means (unvectorized) neingeist 2014-11-17 23:02:10 +0100
f8c0087ff3 Find closest centroids neingeist 2014-11-17 23:01:44 +0100
229023b69c Add exercise 7 neingeist 2014-11-17 21:58:10 +0100
348d6325cb Email feature extraction neingeist 2014-11-13 23:50:52 +0100
f0d4b4d208 Preprocess email neingeist 2014-11-13 23:47:04 +0100
203cbc997c Implement grid search and determine best parameters for C and sigma neingeist 2014-11-13 23:34:04 +0100
e67166bc8e Implement Gaussian kernel neingeist 2014-11-13 22:59:48 +0100
7ab47a4d35 Add exercise 6 neingeist 2014-11-10 23:44:39 +0100
2ab445f9a8 Compute the test set error neingeist 2014-11-06 13:49:17 +0100
78830aaea7 Validation curve function neingeist 2014-11-06 13:31:44 +0100
3751214442 Use lambda=1 for regularizing the polynomial fit neingeist 2014-11-06 12:23:09 +0100
717ea8c788 Polynomial feature mapping neingeist 2014-11-06 12:13:13 +0100
1cc58802eb Learning curve function neingeist 2014-11-06 12:04:53 +0100
90f2928cee Move .gitignore to top-level directory neingeist 2014-11-06 01:19:29 +0100
2d6da3e3d4 Regularized linear regression gradient neingeist 2014-11-06 01:12:38 +0100
6530916642 Regularized linear regression cost function neingeist 2014-11-06 00:53:49 +0100
d93d111106 Add programming exercise 5 neingeist 2014-11-05 11:20:50 +0100
eccdcc0d81 Regularized NN gradient neingeist 2014-11-02 13:48:34 +0100
bdecab8cf8 Implement back propagation neingeist 2014-11-02 13:27:11 +0100
052f0625c3 Random initialization neingeist 2014-11-01 21:22:34 +0100
863f1d7157 Compute the sigmoid gradient neingeist 2014-11-01 21:18:32 +0100
395c5676dc Add regularization to the cost function neingeist 2014-11-01 20:42:58 +0100
f2154a8cc1 Compute cost function for the neural network neingeist 2014-11-01 20:30:58 +0100
be6f3cbdef Move PDFs in top directory neingeist 2014-11-01 14:54:47 +0100
a17f47e396 Add programming exercise 4 neingeist 2014-11-01 14:54:22 +0100
073fbf0204 Add neural network prediction function neingeist 2014-10-23 23:36:15 +0200
3bf3d9fdc3 Add prediction function for one-vs-all classification neingeist 2014-10-23 22:24:55 +0200
cf0d25440c Train num_labels one-vs-all logistic regression classifiers neingeist 2014-10-23 21:17:20 +0200
9117809537 Vectorized regularized logistic regression, again neingeist 2014-10-21 21:20:26 +0200
326a924044 Add exercise 3 neingeist 2014-10-21 20:59:55 +0200
f9243ef593 Simplify regularization term neingeist 2014-10-16 01:03:58 +0200
9e9b9990bb Compute the gradient for regularized logistic regression neingeist 2014-10-15 19:54:07 +0200
f391ac661e Add cost function for regularized logistic regression neingeist 2014-10-15 10:10:30 +0200
224a17e4d3 Plot negative samples in red neingeist 2014-10-14 10:19:09 +0200
c0b4d95f75 Predict neingeist 2014-10-14 10:14:29 +0200
31c4ac1967 Compute the gradient for logistic regression neingeist 2014-10-14 07:46:10 +0200
8c100a3f49 Compute the cost function for logistic regression neingeist 2014-10-14 07:40:42 +0200
b863a3863e .gitignore ml_login_data.mat neingeist 2014-10-13 23:15:15 +0200
efe94282b4 Compute the sigmoid function neingeist 2014-10-13 23:14:44 +0200
d52fa95328 Plot the data neingeist 2014-10-13 23:03:23 +0200
580e52ddc5 Initial commit of ex2 neingeist 2014-10-13 22:56:53 +0200
ba377e29ba Linear regressing using the normal equation neingeist 2014-10-02 22:48:53 +0200
ad9ab582de Gradient descent for multiple features neingeist 2014-10-02 22:33:18 +0200
3dc8897634 Compute cost for multiple variables neingeist 2014-10-02 22:32:56 +0200
e38033c00d Clean up whitespace neingeist 2014-10-02 22:32:37 +0200
8559c243c5 Normalize features neingeist 2014-10-02 22:20:44 +0200
613220bb3e Clean up whitespace neingeist 2014-10-02 22:20:31 +0200
38064db8b3 Print out the cost function J neingeist 2014-10-02 22:08:08 +0200
2bf076c2fc Gradient descent for one variable neingeist 2014-10-02 21:32:24 +0200
514431e135 .gitignore login data neingeist 2014-10-01 22:06:15 +0200
14c85aa85c Compute Cost (for one variable) neingeist 2014-10-01 22:05:13 +0200
bc0eb3519f Plot the data neingeist 2014-09-30 22:25:32 +0200
136815f0b7 Warmup exercise neingeist 2014-09-30 22:25:18 +0200
79825e97f4 import exercise 1: IV. Linear Regression with Multiple Variables (Week 2) neingeist 2014-09-30 22:24:33 +0200

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