# coursera stanford machine learning cost

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### Coursera open course notes: "Advice for applying machinelearning", 10 class of machinelearning at Stanford University )"

networks and overfitting: The following is a "small" Neural Network (which has few parameters and is easy to be unfitted ): It has a low computing cost. The following is a "big" Neural Network (which has many parameters and is easy to overfit ): It has a high computing cost. For the problem of Neural Network overfitting, it can be solved through the regularization (λ) method. References:

### StanfordCourseraMachineLearning Programming Job Exercise 5 (regularization of linear regression and deviations and variances)

different lambda, the calculated training error and cross-validation error are as follows:Lambda Train error Validation error 0.000000 0.173616 22.066602 0.001000 0.156653 18.597638 0.003000 0.190298 19.981503 0.010000 0.221975 16.969087 0.030000 0.281852 12.829003 0.100000 0.459318 7.587013 0.300000 0.921760 1.000000 2.076188 4.260625 3.000000 4.901351 3.822907 10.000000 16.092213 9.945508The graphic is represented as follows:As

### [MachineLearning] Coursera notes-Support Vector machines

friends, but also hope to get the high people of God's criticism!　　 　　 　　Preface　　[Machine Learning] The Coursera Note series was compiled with notes from the course I studied at the Coursera learning (Andrew ng teacher). The content covers linear regression, logistic regre

### MachineLearning| Andrew ng| Coursera Wunda MachineLearning Notes

continuously updating theta. Map Reduce and Data Parallelism: Many learning algorithms can be expressed as computing sums of functions over the training set. We can divide up batch gradient descent and dispatch the cost function for a subset of the data to many different machines So, we can train our algorithm in parallel. Week 11:Photo OCR: Pipeline: Tex

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### StanfordMachineLearning---The sixth lecture. How to choose machineLearning method, System _ Machinelearning

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

### StanfordMachineLearning---The seventh lecture. MachineLearning System Design _ machinelearning

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

### Note for Coursera "Machinelearning" 1 (1) | What are machinelearning?

What are machine learning?The definitions of machine learning is offered. Arthur Samuel described it as: "The field of study that gives computers the ability to learn without being explicitly prog Rammed. " This was an older, informal definition.Tom Mitchell provides a more modern definition: 'a computer program was sa

### [MachineLearning] Coursera ml notes-Logistic regression (logistic Regression)

IntroductionThe Machine learning section records Some of the notes I've learned about the learning process, including linear regression, logistic regression, Softmax regression, neural networks, and SVM, and the main learning data from Standford Andrew Ms Ng's tutorials in Coursera

### StanfordMachineLearning---the eighth lecture. Support Vector Machine Svm_ machinelearning

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines Support vector machine), clust

### StanfordMachineLearning---The sixth week. Design of learning curve and machinelearning system

model and re-experiment to optimize them. (ii) Criteria for numerical evaluation of machine learning algorithms 1. Cross-validation set error (accuracy) This is a good idea, the design of the fitting function if the cross-validation set test error is very large, then certainly not a good learning algorithm; However, is not that the error is must not must be a g

### Stanford University public Class machinelearning: Machines Learning System Design | Data for machinelearning (the learning algorithm behaves better when the volume is large)

For the performance of four different algorithms in different size data, it can be seen that with the increase of data volume, the performance of the algorithm tends to be close. That is, no matter how bad the algorithm, the amount of data is very large, the algorithm can perform well.When the amount of data is large, the learning algorithm behaves better:Using a larger set of training (which means that it is impossible to fit), the variance will be l

### Excellent materials for getting started with MachineLearning: original handouts of the Stanfordmachinelearning course (including open course videos)

Original handout of Stanford Machine Learning Course This resource is the original handout of the Stanford machine learning course, which is AndrewNg said that a total of 20 PDF files cover some important models, algorithms, and

### "MATLAB" machinelearning (Coursera Courses Outline &amp; Schedule)

learning Machine Learning System Design Programming Exercise 5:regularized Linear Regression and Bias v.s. VarianceBest and Most Recent SubmissionScore100 / 100 points earned PASSEDSubmitted on 11 七月 2015 在 3:28 凌晨Part Name Score1 Regularized linear regression cost function 25 / 252 Regularized lin

### Notes of machineLearning (Stanford), Week 6, Advice for applying machinelearning

are as follows:Lambda Train error Validation error 0.000000 0.173616 22.066602 0.001000 0.156653 18.597638 0.003000 0.190298 19.981503 0.010000 0.221975 16.969087 0.030000 0.281852 12.829003 0.100000 0.459318 7.587013 0.300000 0.921760 1.000000 2.076188 4.260625 3.000000 4.901351 3.822907 10.000000 16.092213 9.945508 Training errors, cross-validation errors, and relationships between lambda graphs are represented as follows:When th

### Coursera Open Class MachineLearning: Linear Regression with multiple variables

regression. The root number can also be selected based on the actual situation.Regular Equation In addition to Iteration Methods, linear algebra can be used to directly calculate \$ \ matrix {\ Theta} \$. For example, four groups of property price forecasts: Least Squares \$ \ Theta = (\ matrix {x} ^ t \ matrix {x}) ^ {-1} \ matrix {x} ^ t \ matrix {y} \$Gradient Descent, advantages and disadvantages of regular equations Gradient Descent: Desired stride \$ \ Alpha \$; Multiple iterations are requ

### MachineLearning-Stanford: Learning note 1-motivation and application of machinelearning

training set is appropriate.3. No supervised learningExample: In the case of the tumour above, the point in the figure does not know the correct answer, but is from you to find a certain structure, that is, clustering .Applied in the fields of biological genetic engineering, image processing, computer vision, etc.Example: Cocktail party issuesPick up the sounds you're interested in during a noisy cocktail partyUse two different positions to separate the sound from different positionscan also be

### Neural Network jobs: NN LearningCourseramachinelearning (Andrew Ng) WEEK 5

)/m; at End - End - -%size (J,1) -%size (J,2) - ind3 = A3-Ty; -D2 = (D3 * THETA2 (:,2: End)). *sigmoidgradient (z2); toTheta1_grad = Theta1_grad + d2'*a1/m; +Theta2_grad = Theta2_grad + d3'*a2/m; - the% ------------------------------------------------------------- *jj=0; \$ Panax Notoginseng forI=1: Size (Theta1,1) - forj=2: Size (Theta1,2) theJJ = JJ + Theta1 (i,j) *theta1 (i,j) *lambda/(m*2); + End A End theSize (Theta1,1); +Size (Theta1,2); - \$ forI=1: Size (THETA2,1) \$

### StanfordMachineLearning---sixth lecture. How to choose machinelearning method and system

Original: http://blog.csdn.net/abcjennifer/article/details/7797502This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vector machines), clustering, dimensionality reduc

### StanfordMachineLearning Open Course Notes (14th)-large-scale machinelearning

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. Learning with large datasets ( Big Data Learning ) The importance of data volume has been mentioned in the previous lecture on machine learning design. Remember this sentence: It is not who has the best algorithm that w

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