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networks and overfitting:
The following is a "small" Neural Network (which has few parameters and is easy to be unfitted ):
It has a low computing cost.
The following is a "big" Neural Network (which has many parameters and is easy to overfit ):
It has a high computing cost. For the problem of Neural Network overfitting, it can be solved through the regularization (λ) method.
References:
Machine Learning

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

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

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

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

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

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
Download

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

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

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

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

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

, 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

-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 (

, 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.

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