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 and online courses such as UFLDL Tutorial,stanford cs231n and Tutorial, as well as a large number of online related materials (listed later). PrefaceThis article mainly int
Week 1 Practice quizhelp Center
Warning:the hard deadline has passed. You can attempt it, but and you won't be. You are are welcome to try it as a learning exercise. In accordance with the Coursera Honor Code, I certify this answers here are I own work. Question 1 Consider the instantiation of the vector space model where documents and queries are represented as term Ency vectors. Assume we have the following query and two documents: Q = "Future of on
1. What is a special course (specializations)?If you want to learn a major that you do not understand, you can study according to the special course arrangement. Coursera Special Course collects a field of curriculum, and according to the Order of teaching, it is very suitable for the new people who don't feel well.2. Program Design and algorithmThis special course is a computer Foundation course published by Peking University in
(Datasets) data (IRIS)#Exploratory Analysisnames (Iris) head (IRIS)#The following attempts to take Virginica,speal. The method of length is all wrongiris[,2]iris[iris$species=="virginica", 2]mean (iris[iris$species=="virginica", 2])##the above is Error,not correct##tapply (Test$sepal.length,test$species,mean)#using Species.mean to group vectors, this method is feasible, but the above method is necessary to look at the errorLibrary (Datasets) data (Mtcars) #以下为做某个题时的若干测试. And a trial-and-error l
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 video can be viewed or downloaded on Coursera
NTU-Coursera ml: HomeWork 1 Q15-20Question15
The training data format is as follows:
The input has four dimensions, and the output is {-1, + 1 }. There are a total of 400 data records.
The question requires that the weight vector element be initialized to 0, and then "Naive Cycle" is used to traverse the training set. When the iteration is stopped, the weight vector is updated several times.
The so-called "Naive Cycle" means that after an error i
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 coursera-Wunda-Machine learning-all programming practice answers
In this exercise, you will implement the K-means clustering algorithm and apply it to compressed images. In the second section, y
We recommend the responsive programming course on Coursera, an advanced Scala language course. At the beginning of the course, we proposed an Application Scenario: constructing a JSON string. If you do not know the JSON string, you can simply Google it. To do this, we define the following classes
abstract class JSON case class JSeq(elems: List[JSON]) extends JSON case class JObj(bindings: Map[String, JSON]) extends JSON case class JNum(num: Double) e
#include using namespacestd;/*int Wanmeifugai (int n) {if (n%2) {return 0; } else if (n==2) {return 3; }else if (n = = 0) return 1; else return (3*3) *wanmeifugai (n-4);}*///The following is a reference to the online program/*Ideas: Citation:http://m.blog.csdn.net/blog/njukingway/20451825First: F (n) = 3*f (n-2) + ... f (n) = 3*f (n-2) + 2*f (n-4) +....//just now our recursion is pushed in the smallest unit (3 blocks), but there are large units of small units (6, 9, 12 blocks, etc.) There
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 machine learning.
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
, 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 machine learning, please
function and map the given set to another set. The signature is as follows:
def map(s: Set, f: Int => Int): Set
The second parameter f is used to map the elements of the original set to the functions of the new set (first-class citizen !)
The question looks simple, just to judge whether the elements in s are equal to the input integer after f ing.
This includes two steps:
1. Is there any element in s that meets a specific condition (assertion )?
2. The specific condition (assertion) is mapped t
, 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 threshold, such as 0.001, to determine convergen
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