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

Stanford University machine Learning lesson 10 "Neural Networks: Learning" study notes. This course consists of seven parts: 1) Deciding what to try next (decide what to do next) 2) Evaluating a hypothesis (Evaluation hypothesis) 3) Model selection and training/validation/te

Stanford University public Class machine learning: Machines Learning System Design | Data for machine learning (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

[Original] Andrew Ng chose to fill in the blanks in Coursera for Stanford machine learning.

Week 2 gradient descent for multiple variables [1] multi-variable linear model cost function Answer: AB [2] feature scaling feature Scaling Answer: d 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: [Original] Andrew Ng chose to fill in the blanks in Coursera for Sta

Stanford Coursera Machine Learning 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

Stanford University public Class machine learning: Advice for applying machines learning-deciding to try next (how to determine the most appropriate and correct method when designing a machine learning system)

feeling that most people choose to choose one of these methods casually, such as they would say "let's find some more data" and then spend six months collecting a lot of data, and then maybe another person said, " Let's find some more features in the data from these houses. " A lot of people spend at least six months to complete one of their random choices, and after six months or more, they regret to find that they have chosen a way of no return.Stanford U

Stanford University public Class machine learning: Machines Learning System Design | Error metrics for skewed classes (definition of skew class issues and evaluation measures for skew class issues: precision ratio (precision) and recall rate (recall))

classification model, which gives us a better evaluation value and gives us a more direct way to evaluate the good and bad of the model. One last thing to keep in mind, in the definition of precision and recall, we define precision and recall rates, and we habitually use Y=1 to show that this class appears very little. So if we try to detect a very rare situation, like cancer. I hope it's a rare situation where precision and recall are defined as Y=1 rather than y=0, as some of the fewer classe

Stanford University public Class machine learning: Advice for applying machines learning | Learning curves (Improved learning algorithm: the relationship between high and high variance and learning curve)

to the right in this image. We can generally see the two learning curves, the two curves of blue and red are approaching each other. Therefore, if we extend the curve to the right, it seems that the training set error is likely to increase gradually. The cross-validation set error will continue to decline. Of course, we are most concerned with cross-validation set errors or test set errors. So from this picture, we can basically predict that if we co

Stanford University public Class machine learning: Advice for applying machines learning-evaluatin a phpothesis (how to evaluate the assumptions given by the learning algorithm and how to prevent overfitting or lack of fit)

assumptions tend to be 0, but the actual labels are 1, both of which indicate a miscarriage of judgment. Otherwise, we define the error value as 0, at which point the value is assumed to correctly classify the sample Y.Then, we can use the error rate errors to define the test error, that is, 1/mtest times the error rate errors of H (i) (xtest) and Y (i) (sum from I=1 to Mtest).Stanford University public Cl

Stanford University Machine Learning public Class (II): Supervised learning application and gradient descent

mathematical expression was unfolded using Taylor's formula, and looked a bit ugly, so we compared the Taylor expansion in the case of a one-dimensional argument.You know what's going on with the Taylor expansion in multidimensional the [1] type, the higher order infinitesimal can be ignored, so the [1] type is taken to the minimum value,should maketake the minimum-this is the dot product (quantity product) of two vectors, and in what case is the value minimal? look at the two vec

Stanford University public Class machine learning: Machines Learning System Design | Trading off precision and recall (F score formula: How to balance (trade-off) precision and recall values in a learning algorithm)

take an average of this evaluation mode.It is a useful algorithm to use the F-score algorithm to evaluate both precision and recall rates . The PR of the molecule determines that the precision ratio (P) and recall (R) must be large at the same time to ensure that the F score values are larger. If the precision ratio or recall rate is very low, close to 0, the direct result of the PR value is very low, approaching 0, that is, F score is also very low.At this point we compare three algorithms, we

Stanford University-machine learning public class-2. Supervised learning applications • Gradient descent

be able to find the global optimal solution.When the training sample is very large, each update parameter needs to traverse all the sample calculation total error, so that the learning speed is too slow; this time the random gradient descent algorithm that calculates the error update parameters of a sample is usually more thanThe batch gradient descent method is faster. (Theoretically, there is no guarantee that the random gradient descent can conver

Stanford University public Class machine learning: Advice for applying machines learning | Deciding what to try Next (Revisited) (for high-deviation, high-variance resolution and the choice of hidden layers)

default is to use a hidden layer is a reasonable choice, but if you want to choose the most appropriate layer of hidden layer, you can also try to split the data into training sets, validation sets and test sets, and then try to use a hidden layer of neural network to train the model. Then try two, three hidden layers, and so on. Then see which neural network behaves best on the cross-validation set. That means you get three neural network models, one, two, and three hidden layers, respectively

Stanford University public Class machine learning: Neural Networks learning-autonomous Driving example (automatic driving example via neural network)

is going when it is initialized, or we don't know where the driving direction is, only after the learning algorithm has been running long enough that the white section appears in the entire gray area, showing a specific direction of travel. This means that the neural network algorithm at this time has chosen a clear direction of travel, not like the beginning of the output of a faint light gray area, but the output of a white section.Stanford

Machine Learning Stanford University Open Class (1)

Machine learning defines learning definitionArthur Samuel (1959). Machine Learning:field of study, gives computers the ability to learn without being explicitly programmed.There is no clear programming case to make the computer capable of learning the field of study.Four par

Stanford University Machine Learning public Class (VI): Naïve Bayesian polynomial model, neural network, SVM preliminary

Terryj.sejnowski. (c) function interval and geometric interval of support vector machineto understand support vector machines (vectormachine), you must first understand the function interval and the geometry interval. Assume that the dataset is linearly divided. first change the symbol, the category y desirable value from {0,1} to { -1,1}, assuming that the function g is:The objective function H also consists of:Into:wherein, Equation 15 x,θεRn+1, and X0=1. In Equation 16, x,ωεRN,b replaces the

Stanford University public Class machine learning: Neural Network-model Representation (neural network model and Neural Unit understanding)

these matrices, and the θ superscript (j) becomes a wave matrix that controls the action from the first layer to the second or second to the third layer. The first hidden unit calculates its value in this way: A (2) 1 equals the S function or S-excitation function, also called the logical excitation function, which acts on the linear combination of this input. The second hidden unit equals the value of the S function on this linear combination. The parameter matrix controls the mapping from thr

Stanford Machine Learning---The sixth lecture. How to choose machine Learning method, System _ Machine learning

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

Excellent materials for getting started with Machine Learning: original handouts of the Stanford machine learning 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

[Machine Learning] 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

Machine Learning| Andrew ng| Coursera Wunda Machine Learning 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: Text detection Character segmentation Ch

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