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