إنشاء حساب
الرئيسية
الكورسات
تعلم الآلة
دورة تعلم الآلة - مقدمة من البروفيسور أندرو نج
Lecture 0107 Cost function intuition I

Lecture 0107 Cost function intuition I

دورة تعلم الآلة - مقدمة من البروفيسور أندرو نج

الدرس السابق الدرس التالي

محتوى الكورس

جميع الدروس
102 درس
40:51 Lecture 01-01 Introduction
15:50 Lecture 01-02 Linear regression with one variable
00:42 Lecture 01-03 Linear Algebra review
01:15 Lecture 02-01 Linear Regression with multiple variables
23:16 Lecture 02-02 Octave Tutorial
12:24 Lecture 03-01 Logistic Regression
39:07 Lecture 03-02 Regularization
02:36 Lecture 04 Neural Networks Representation
17:36 Lecture 05 Neural Networks Learning
03:14 Lecture 06-01 Advice for applying machine learning
59:32 Lecture 06-02 Machine learning system design
37:35 Lecture 07 Support Vector Machines
39:05 Lecture 08-01 Clustering
07:09 Lecture 08-02 Dimensionality Reduction
06:55 Lecture 0101 Welcome
07:15 Lecture 0102 What is machine learning
12:30 Lecture 0103 Supervised Learning
14:14 Lecture 0104 Unsupervised Learning
08:11 Lecture 0105 Model representation
08:13 Lecture 0106 Cost function
11:10 Lecture 0107 Cost function intuition I
08:49 Lecture 0108 Cost function intuition II
11:31 Lecture 0109 Gradient descent
11:51 Lecture 0110 Gradient descent intuition
10:21 Lecture 0111 Gradient descent for linear regression
05:50 Lecture 0112 What's next
08:46 Lecture 0113 Matrices and vectors
06:54 Lecture 0114 Addition and scalar multiplication
13:40 Lecture 0115 Matrix-vector multiplication
11:10 Lecture 0116 Matrix-matrix multiplication
09:03 Lecture 0117 Matrix multiplication properties
11:14 Lecture 0118 Inverse and transpose
08:23 Lecture 0201 Multiple features
05:05 Lecture 0202 Gradient descent for multiple variables
08:53 Lecture 0203 Gradient descent in practice I: Feature Scaling
08:59 Lecture 0204 Gradient descent in practice II: Learning rate
07:40 Lecture 0205 Features and polynomial regression
16:18 Lecture 0206 Normal equation
05:59 Lecture 0207 Normal equation and non-invertibility(optional)
14:00 Lecture 0208 Basic operations
16:08 Lecture 0209 Moving data around
13:15 Lecture 0210 Computing on data
09:39 Lecture 0211 Plotting data
12:56 Lecture 0212 For, while, if statements, and functions
03:34 Lecture 0213 Working on and submitting programming exercises
13:49 Lecture 0214 Vectorization
08:09 Lecture 0301 Classification
07:25 Lecture 0302 Hypothesis Representation
14:50 Lecture 0303 Decision boundary
11:26 Lecture 0304 Cost function
10:15 Lecture 01-01 Introduction
14:07 Lecture 01-02 Linear regression with one variable
06:16 Lecture 01-03 Linear Algebra review
09:43 Lecture 02-01 Linear Regression with multiple variables
10:11 Lecture 02-02 Octave Tutorial
10:41 Lecture 03-01 Logistic Regression
08:34 Lecture 03-02 Regularization
09:36 Lecture 04 Neural Networks Representation
07:48 Lecture 05 Neural Networks Learning
12:02 Lecture 06-01 Advice for applying machine learning
11:47 Lecture 06-02 Machine learning system design
07:16 Lecture 07 Support Vector Machines
10:21 Lecture 08-01 Clustering
03:52 Lecture 08-02 Dimensionality Reduction
06:44 Lecture 0101 Welcome
12:00 Lecture 0102 What is machine learning
12:45 Lecture 0103 Supervised Learning
07:48 Lecture 0104 Unsupervised Learning
11:38 Lecture 0105 Model representation
06:52 Lecture 0106 Cost function
13:24 Lecture 0107 Cost function intuition I
06:31 Lecture 0108 Cost function intuition II
05:51 Lecture 0109 Gradient descent
07:36 Lecture 0110 Gradient descent intuition
12:04 Lecture 0111 Gradient descent for linear regression
07:43 Lecture 0112 What's next
11:21 Lecture 0113 Matrices and vectors
11:54 Lecture 0114 Addition and scalar multiplication
06:51 Lecture 0115 Matrix-vector multiplication
09:30 Lecture 0116 Matrix-matrix multiplication
13:13 Lecture 0117 Matrix multiplication properties
11:36 Lecture 0118 Inverse and transpose
14:06 Lecture 0201 Multiple features
11:10 Lecture 0202 Gradient descent for multiple variables
14:48 Lecture 0203 Gradient descent in practice I: Feature Scaling
10:37 Lecture 0204 Gradient descent in practice II: Learning rate
19:42 Lecture 0205 Features and polynomial regression
15:45 Lecture 0206 Normal equation
15:44 Lecture 0207 Normal equation and non-invertibility(optional)
21:03 Lecture 0208 Basic operations
03:18 Lecture 0209 Moving data around
12:33 Lecture 0210 Computing on data
07:05 Lecture 0211 Plotting data
07:50 Lecture 0212 For, while, if statements, and functions
08:23 Lecture 0213 Working on and submitting programming exercises
10:10 Lecture 0214 Vectorization
05:28 Lecture 0301 Classification
09:06 Lecture 0302 Hypothesis Representation
15:15 Lecture 0303 Decision boundary
03:55 Lecture 0304 Cost function
10:31 Lecture 01-01 Introduction
12:49 Lecture 01-02 Linear regression with one variable