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This section describes the core of machine learning, the fundamental problem-the feasibility of learning. As we all know about machine learning, the ability to measure whether a machine learni
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-learning Exercise 7--k-means and PCA
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, 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
Original: http://blog.csdn.net/abcjennifer/article/details/7834256This 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
Before the machine learning is very interested in the holiday cannot to see Coursera machine learning all the courses, collated notes in order to experience repeatedly.I. Introduction (Week 1)-What's machine learningThere is no un
Public Course address:Https://class.coursera.org/ml-003/class/index
INSTRUCTOR:Andrew Ng 1. deciding what to try next (
Determine what to do next
)
I have already introduced some machine learning methods. It is obviously not enough to know the specific process of these methods. The key is to learn how to use them. The so-called best way to master knowledge is to put it into practice. Consider the ear
This is a machine learning course that coursera on fire, and the instructor is Andrew Ng. In the process of looking at the neural network, I did find that I had a problem with a weak foundation and some basic concepts, so I wanted to take this course to find a leak. The current plan is to see the end of the neural network, the back is not necessarily seen.Of cour
is that only the input paradigm is provided for this network, and it automatically identifies its potential class rules from those examples. When the study is complete and tested, it can also be applied to new cases.
A typical example of unsupervised learning is clustering. The purpose of clustering is to bring together things that are similar, and we do not care what this class is. Therefore, a clustering algorithm usually needs to know how to c
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
m>=10n and uses multiple Gaussian distributions.In practical applications, the original model is more commonly used, the average person will manually add additional variables.If the σ matrix is found to be irreversible in practical applications, there are 2 possible reasons for this:1. The condition of M greater than N is not satisfied.2. There are redundant variables (at least 2 variables are exactly the same, XI=XJ,XK=XI+XJ). is actually caused by the linear correlation of the characteristic
-Learning RateIn the gradient descent algorithm, the number of iterations required for the algorithm convergence varies according to the model. Since we cannot predict in advance, we can plot the corresponding graphs of iteration times and cost functions to observe when the algorithm tends to converge.Of course, there are some ways to automatically detect convergence, for example, we compare the change value of a cost function with a predetermined thr
-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 (
is close to the global minimum. In fact, you can dynamically adjust the learning rate α= constant 1/(number of iterations + constant 2), so that as the iteration, α gradually reduced, in favor of the final convergence to the global minimum value. However, because "constant 1" and "Constant 2" is not OK, so often set α is fixed.How do you judge the convergence of the model as the iteration progresses? Every 1000 or 5,000 samples, the J value of these
, 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
distribution with the mean value of μ 0 and the covariance matrix of Σ, X | y = 1 follows the multivariate Gaussian distribution where the mean value is μ1 and the covariance matrix is Σ (This will be discussed later ).
The log function for maximum likelihood estimation is recorded as L (ø, μ 0, μ 1, Σ) = Log 1_mi = 1 p (x (I) | Y (I); μ 0, μ 1, Σ) P (Y (I); ø), our goal is to obtain the parameter ø, μ 0, μ 1, Σ to make L (ø, μ 0, 1, Σ) to obtain the maximum value.
The values of the four para
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 mo
-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-
unreasonable. That is, in the past two months the word has not appeared in the mail, it is considered that the probability of 0, unreasonable.Generally speaking, it is unreasonable to think that these events will not happen if they have not been seen before . Solve this problem with Laplace smoothing.4. Laplace SmoothingAccording to the maximum likelihood estimate, p (y=1) = # "1" s/(# "0" s + # "1" s), that is, the probability of Y being 1 is the ratio of the number of 1 in the sample to all s
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