coursera python for everybody

different lambda, the calculated training error and cross-validation error are as follows:Lambda Train error Validation error 0.000000 0.173616 22.066602 0.001000 0.156653 18.597638 0.003000 0.190298 19.981503 0.010000 0.221975 16.969087 0.030000 0.281852 12.829003 0.100000 0.459318 7.587013 0.300000 0.921760 1.000000 2.076188 4.260625 3.000000 4.901351 3.822907 10.000000 16.092213 9.945508The graphic is represented as follows:As

, 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

-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

-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 (

(x.next[c], key, d+1); the returnx; * } $ Panax Notoginseng Public Booleancontains (String key) - { theNode x = Get (root, key, 0); + if(x = =NULL)return false; A returnX.hasword; the } + - PrivateNode get (node X, String key,intd) $ { $ if(x = =NULL)return NULL; - if(d = = Key.length ())returnx; - intc =charAt (key, D); the returnGet (X.next[c], key, d+1); - }Wuyi the Public BooleanHaskeyswi

use of MATLAB. *.4.gradientdescent.mfunction [Theta, j_history] =gradientdescent (X, y, theta, Alpha, num_iters)%gradientdescent performs gradient descent to learn theta% theta = gradientdescent (X, y, theta, Alpha, num_iters) up Dates theta by% taking num_iters gradient steps with learning rate alpha% Initialize Some useful valuesm= Length (y);%Number of training examplesj_history= Zeros (Num_iters,1); forITER =1: Num_iters% ====================== YOUR CODE here ======================% instru

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-machine Learning, Stanford:w

I've been procrastinating for the last time, and I'm going to keep it up today. Programming Title #: Calculating the sum of the edge elements of a matrix Source: POJ (Coursera statement: The exercises completed on POJ will not be counted into Coursera's final results. ) Note: Total time limit: 1000ms memory limit: 65536kB description Enter an integer matrix to compute the sum of elements at the edge of the matrix. The elements of the so-called matrix

Week 3 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 Assume you are using a Unigram language model to calculate the probabilities of phrases. Then, the probabilities of generating the phrases "study text mining" and "text mining study" are not equal, i

Week 3 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 are given a vocabulary composed of only three words: "text", "mining", and "the". Below are the probabilities of two of this three words given by a Unigram model: Word Probability Text 0.4 M

example, you is going to generate an image of the Louvre Museum in Paris (content image C), mixed with a painting By Claude Monet, a leader of the Impressionist movement (style image S). Let's see how you can do this. 2-transfer Learning Neural Style Transfer (NST) uses a previously trained convolutional network, and builds on top of. The idea of using a network trained in a different task and applying it to a new task is called transfer learning. Following the original NST paper (https://arxiv

several chosen LAYERS Arguments: Model--Our TensorFlow model style_layers--A python list containing:-The names of the LA Yers we would like to extract style from-a coefficient for each of them returns:j_sty le--tensor representing a scalar value, style cost defined above by equation (2) "" "# Initialize the overall s Tyle cost J_style = 0 for Layer_name, Coeff in Style_layers # Select the output tensor of the currently SE lected layer out = model[lay

Use Python to master machine learning in four steps and python to master machines in four steps To understand and apply machine learning technology, you need to learn Python or R. Both are programming languages similar to C, Java, and PHP. However, since Python and R are both relatively young and "Far Away" from the CP

5.1 Section cost FunctionThe cost function of a neural network.Review some of the concepts in neural networks:L the total number of layers of the neural network.Number of units of the SL-L layer (excluding deviation units).Category 2 Classification

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 learning algorithm is learning is not how the model behaves on

Extremely light of a semester finally passed, summer vacation intends to learn the big step down this machine learning techniques.The first lesson is the introduction of SVM, although I have learned it before, but I heard a feeling is very rewarding.

This section is about overfitting, listening to the understanding of overfitting more profound than before.First introduced the overfitting, the consequence is that Ein is very small, and eout is very large. Then the causes of overfitting are