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This series is a personal learning note for Andrew Ng Machine Learning course for Coursera website (for reference only)Course URL: https://www.coursera.org/learn/machine-
Operating system Learning notes----process/threading Model----Coursera Course note process/threading model 0. Overview 0.1 Process ModelMulti-Channel program designConcept of process, Process control blockProcess status and transitions, process queuesProcess Control----process creation, revocation, blocking, wake-up 、...0.2 threading ModelWhy threading is introdu
Overview
Cost Function and BackPropagation
Cost Function
BackPropagation algorithm
BackPropagation Intuition
Back propagation in practice
Implementation Note:unrolling Parameters
Gradient Check
Random initialization
Put It together
Application of Neural Networks
Autonomous Driving
Review
Log
2/10/2017:all the videos; Puzzled about Backprogation
2/11/2017:reviewed backpropaga
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.
, i.e., all of our training examples lie perfectly on some straigh T line.
If J (θ0,θ1) =0, that means the line defined by the equation "y=θ0+θ1x" perfectly fits all of our data.
For the To is true, we must has Y (i) =0 for every value of i=1,2,..., m.
So long as any of our training examples lie on a straight line, we'll be able to findθ0 andθ1 so, J (θ0,θ1) =0. It is not a necessary that Y (i) =0 for all of our examples.
We can perfectly predict the value o
, 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
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-
This section is about the nuclear svm,andrew Ng's handout, which is also well-spoken.The first is kernel trick, which uses nuclear techniques to simplify the calculation of low-dimensional features by mapping high-dimensional features. The handout also speaks of the determination of the kernel function, that is, what function K can use kernel trick.In addition, the kernel function can measure the similarity of two features, the greater the value, the
17.1 Study of large data sets17.2 Random Gradient descent method17.3 Miniature Batch gradient descent17.4 Stochastic gradient descent convergence17.5 Online Learning17.6 mapping Simplification and data parallelism 17.1 Study of large data sets 17.2 Stochastic gradient descent method 17.3miniature Batch gradient descent 17.4 stochastic gradient descent convergence 17.5 Online learning 17.6 mapping simplification and data parallelism
learning.In fact, these two states are not completely divided, for example, if we are trading in a lot of fraud, then we study the problem from anomaly detection to supervise learning.Exercise: Intuitive judgment of two situationsChoosingwhat Features to useThe previous approach is to assume that the data satisfies the Gaussian distribution, and also mentions that if the distribution is not Gaussian distribution, the above method can be used, but if we convert the distribution to approximate Ga
Machine Learning-Overview of common matlab programming commands
-- Summary from ng-ml-class octave/MATLAB tutorial CourseraA. basic operations and moving data around1 in command line mode, you can use Shift + press enter to append the next line to output 2 length command to apply to the matrix, and return a higher one-dimensional dimension3 help + command is the
[Introduction to machine learning] Li Hongyi Machine Learning notes-9 ("Hello World" of deep learning; exploring deep learning)
PDF
Video
Keras
Example appl
Ng-init is to execute the given expression for angular, initialize the value of the variable'UTF-8'> ' ng-init= ' mytest= "Hello World"> {{myTest}}This initializes the value of the mytest, Ng-app does not set the value, if set, will JS, otherwise it will have to errorAngular some small notes (middle) of
Machine learning Notes (iii) multivariable linear regression
Note: This content resource is from Andrew Ng's machine learning course on Coursera, which pays tribute to
increase or reduce the number of example (change 100 to 1000 or 10, etc.), reduce or increase the learning rate.elearning (Online learning)The previous algorithm has a fixed training set to train the model, when the model is well trained to classify and return the future example. Online learning is different, it updates the model parameters for each new example,
Machine learning notes (b) univariate linear regression
Note: This content resource is from Andrew Ng's machine learning course on Coursera, which pays tribute to
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