-Gradient descentThe gradient descent algorithm is an algorithm for calculating the minimum value of a function, and here we will use the gradient descent algorithm to find the minimum value of the cost function.The idea of a gradient descent is that we randomly select a combination of parameters and calculate the cost function at the beginning, and then we look for the next combination of parameters that will reduce the value of the cost function.We continue this process until a local minimum (
, the weight of the high-weighted data is increased by 1000 times times the probability, which is equivalent to replication. However, if you are traversing the entire test set (not sampling) to calculate the error, there is no need to modify the call probability, just add the weights of the corresponding errors and divide by N. So far, we have expanded the VC Bound, which is also set up on the issue of multiple classifications!SummaryFor more discussion and exchange on
would the Vectorize this code to run without all for loops? Check all the Apply.
A: v = A * x;
B: v = Ax;
C: V =x ' * A;
D: v = SUM (A * x);
Answer: A. v = a * x;
v = ax:undefined function or variable ' Ax '.
4.Say you has a vectors v and Wwith 7 elements (i.e., they has dimensions 7x1). Consider the following code:
z = 0;
For i = 1:7
Z = z + V (i) * W (i)
End
Which of the following vectorizations correctly compute Z? Check all the Apply.
Week 2 gradient descent for multiple variables
[1] multi-variable linear model cost function
Answer: AB
[2] feature scaling feature Scaling
Answer: d
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[Original] Andrew Ng chose to fill in the blanks in Coursera for Stanford
-Gradient descent for linear regressionHere we apply the gradient descent algorithm to the linear regression model, we first review the gradient descent algorithm and the linear regression model:We then expand the slope of the gradient descent algorithm to the partial derivative:In most cases, the linear regression model cost function is shaped like a convex body, so the local minimum value is equivalent to the global minimum:The following is the entire convergence and parameter determination pr
Overview
photo OCR
problem Description and Pipeline
sliding Windows
getting Lots of data and Artificial data
ceiling analysis:what part of the Pipeline to work on Next
Review
Lecture Slides
Quiz:Application:Photo OCR
Conclusion
Summary and Thank You
Log
4/20/2017:1.1, 1.2;
Note
Ocr?
...
Coursera-
-Normal equationSo far, the gradient descent algorithm has been used in linear regression problems, but for some linear regression problems, the normal equation method is a better solution.The normal equation is solved by solving the following equations to find the parameters that make the cost function least:Assuming our training set feature matrix is x, our training set results are vector y, then the normal equation is used to solve the vector:The following table shows the data as an example:T
Extremely light of a semester finally passed, summer vacation intends to learn the big step down this machine learning techniques.The first lesson is the introduction of SVM, although I have learned it before, but I heard a feeling is very rewarding. The blogger sums up a ballpark figure, and the specifics areTo listen: http://www.cnblogs.com/bourneli/p/4198839.htmlThe blogger sums it up in detail: http://w
)/∂ (θ (1) JK) is tested for gradients. After the partial derivative code does not have a problem, close the Gradient check section code.6. Use gradient descent or other advanced algorithms to perform reverse propagation to find the θ values for minimizing j (θ).This paper describes the gradient descent algorithm in neural networks: starting from the random initial point, descending step by step, until the local optimal value is obtained. Algorithms such as gradient descent can at least guarante
dimension.Finally, we propose a method for solving overfitting, including data cleaning/pruning, data hinting, regularization (regularization), confirmation (validation), andTo drive for example to illustrate the role of these methods, the latter two methods are also the contents of the following two lessons.Data cleaning/pruning is to correct or delete the wrong sample points, processing is simple, but usually such sample points are not easy to find.Data hinting generate more sample numbers by
This section is about regularization, in the optimization of the use of regularization, in class when the teacher a word, not too much explanation. After listening to this class,To understand the difference between a good university and a pheasant university. In short, this is a very rewarding lesson.First of all, we introduce the reason for regularization, simply say that the complex model with a simple model to express, as to how to say, there is a series of deduction hypothesis, very creative
-Feature ScalingWhen we are faced with multidimensional feature problems, we need to ensure that the multidimensional features have similar scales, which will help the gradient descent algorithm to converge faster.Take the housing price forecast problem as an example, assuming that the two characteristics we use, namely the size of the house and the number of rooms, the size value range is 0-2000 square feet, and the value of the room number is 0-5, which causes the gradient descent algorithm to
-Cost functionFor the training set and our assumptions, we will consider how to determine the coefficients in the assumptions.What we are going to do now is to choose the right parameters, and the selection of parameters directly affects the accuracy of the resulting straight line for the training set description. The difference between the predicted value and the actual value in the training set is the modeling error (Modeling error).the cost function is defined by calculating the sum of square
than or equal to 0, which is greater than or equal to 3 o'clock, the model predicts y = 1.We can draw a straight line, which is the dividing line of our model, separating the area predicted to 1 and the area predicted as 0.What kind of model would be appropriate if our data were to be presented in the following circumstances?Because curves are required to separate areas of y = 0 and y = 1, we need two-character:Assuming that the parameter is [-1 0 0 1 1], then we get the decision boundary is ex
Mainly for the week content: large-scale machine learning, cases, summary(i) Random gradient descent methodIf there is a large-scale training set, the normal batch gradient descent method needs to calculate the sum of squares of errors across the entire training set, which is a very large computational cost if the learning method needs to iterate 20 times.First,
Mainly for the sixth week Content machine learning application recommendations and system design.What to do nextWhen training good one model, predicting unknown data discovery, how to improve it?
Get more examples of training
Try to reduce the number of features
Try to get more features
Try adding two-item features
Try to reduce the degree of normalization λ
Try to increase the
-Unsupervised learningIn supervised learning, whether it is a regression problem or a classification problem, we use the data to have a clear label or the corresponding prediction results.In unsupervised learning, our existing data have no corresponding results or labels, and some are just features. Therefore, the problem to be solved by unsupervised learning is
I've been talking about why machines can learn, and starting with this lesson are some basic machine learning algorithms, i.e. how machines learn.This lesson is about linear regression, starting with the minimization of Ein, introducing the Hat Matrix to understand the geometric meaning. Finally, the linear regression and binary classification are compared, and the reason why linear regression can be used t
Mainly for the ninth week content: Anomaly detection, recommendation system(i) Anomaly detection (DENSITY estimation) kernel density estimation ( Kernel density estimation X (1) , X (2) ,.., x (m) If the data set is normal, we want to know the new data X (test) p (x) After density estimation, it is a common method to select a probability threshold to determine whether it is an anomaly, which is often used in anomaly detection. Such as:
Gaussian distributionThe Gaussian k
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