machine learning certification by stanford university coursera
machine learning certification by stanford university coursera
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)Ans =01Note: The first data above the main diagonal is taken as the starting data, and is sorted in diagonal order as a column vector form4, V = diag (x) returns the element on the main diagonal of matrix X, similar to Diag (X,K), Case 5 of K=0:V=[1 0 0;0 3 0;0 0 3];Diag (v)Ans =133or instead:V=[1 0 3;2 3 1;4 5 3];Diag (v)Ans =133Note: The data of the main diagonal is taken out as a column vector form5,diag (diag (X))Take the diagonal element of the X-matrix and construct a diagonal matrix with
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
Second lecture: Simple word vector representation: Word2vec, Glove (easy word vector representations:word2vec, Glove)Reprint please specify the source and retention link "I love Natural Language processing": http://www.52nlp.cnThis article link address: Stanford University deep Learning and Natural language processing second: Word vectorRecommended Reading materi
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
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
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
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
Coursera Andrew Ng Machine learning is really too hot, recently had time to spend 20 days (3 hours a day or so) finally finished learning all the courses, summarized as follows:(1) Suitable for getting started, speaking the comparative basis, Andrew speaks great;(2) The exercise is relatively easy, but to carefully con
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
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
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 (
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