coursera algorithms

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

expanded into vector form thetavec:thetavec=[theta1 (:); Theta2 (:); Theta3 (:)]Thetavec the first 1-110 elements of a vector form into a matrix form 10*11 Theta1:theta1=reshape (Thetavec (1:110), 10,11)Overview of basic algorithms:In the 1.fminunc function, the initial weight matrix parameter θ (1), θ (2), θ (2) are expanded into a vector form of initialtheta. Pass Initialtheta as a formal parameter to the Fminunc function.2. In the cost function, the vector form of the Thetavec is converted t

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.

(w ')Description W over fitting3 Sources of errorNoise, Bias, Variance1. Noise NoiseOf an inherent, irreducible, or reduced nature.　　　2, Bias Deviation　　　　　　The simpler the model, the greater the deviation　　The more complex the model, the smaller the deviation3. Variance Variance　　　　Simple model, small variance　　Complex model, large variance　　Deviations and variance tradeoffs, deviations and variances cannot be calculated　　　　Training error and the amount of test data, fixed model complexity, a

-Normal equationSo far, the gradient descent algorithm has been used in linear regression problems, but for some linear regression problems, the normal equation method is a better solution.The normal equation is solved by solving the following equations to find the parameters that make the cost function least:Assuming our training set feature matrix is x, our training set results are vector y, then the normal equation is used to solve the vector:The following table shows the data as an example:T

Week 4 Quizhelp Center Warning:the hard deadline has passed. You can attempt it, Butyou won't get credit for it. 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 Which of the following is nottrue about GFS? The GFS keeps multiple replicas of the same file chunk. The file data transfer happens directly between the GFS client and the GFS chunkservers

Week 2 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 Suppose a query has a total of 4 relevant documents in the collection. System A and System B have each retrieved, and the relevance status of the ranked lists is shown below: System A: [-----------]

cost function least.The algorithm is:After derivation, get:Note: Although the resulting gradient descent algorithm appears to be the same as the gradient descent algorithm for linear regression, the hypothetical function here differs from the linear regression, so it is actually different. In addition, it is still necessary to perform feature scaling before applying the gradient descent algorithm.In addition, there are some alternatives to the gradient descent algorithm:In addition to the gradi

the true assertion Alpha (Alpha) It's a digital control called learning rate. How much update do you take? If Alpha is large, it has a larger descent rate, if alpha is small, the magnitude is smaller derivative -then explain in detail a clever implementation of gradient descent algorithm The following processing θ0 and θ1 for j = 0 and J = 1 means that the values of θ0 and θ1 are also updated to implement. Calculates the right-hand side of the equation for θ0 and θ1 So we need a temp value an

I've been talking about why machines can learn, and starting with this lesson are some basic machine learning algorithms, i.e. how machines learn.This lesson is about linear regression, starting with the minimization of Ein, introducing the Hat Matrix to understand the geometric meaning. Finally, the linear regression and binary classification are compared, and the reason why linear regression can be used to do binary classification is explained. The

Mainly for the week content: large-scale machine learning, cases, summary(i) Random gradient descent methodIf there is a large-scale training set, the normal batch gradient descent method needs to calculate the sum of squares of errors across the entire training set, which is a very large computational cost if the learning method needs to iterate 20 times.First, you need to determine whether a large-scale training set is necessary. When we do need a large-scale training set, we can try to replac

-Unsupervised learningIn supervised learning, whether it is a regression problem or a classification problem, we use the data to have a clear label or the corresponding prediction results.In unsupervised learning, our existing data have no corresponding results or labels, and some are just features. Therefore, the problem to be solved by unsupervised learning is to find out whether the data can be divided into different groups.A typical problem of unsupervised learning is clustering problems, su

ResnetsThe identity blockThe convolutional block (you can use this type of block when the input and output dimensions don ' t match up. The conv2d layer in the shortcut path was used to resize the input xx to a different dimension, so that the dimensions MATC H up in the final addition needed to add the value of the shortcut to the main path. (this plays a similar role as the Matrix Wsws discussed in lecture.) For example, to reduce the activation dimensions's height and width by a factor of 2,

Deep Learning art:neural Style Transfer Welcome to the second assignment of this week. In this assignment, you'll learn about neural Style Transfer. This algorithm is created by Gatys et al. (https://arxiv.org/abs/1508.06576). in this assignment, you'll:-Implement the neural style transfer algorithm-Generate novel artistic images using your algorithm Most of the algorithms you ' ve studied optimize a cost function to get a set of parameter values. I

Recursive Algorithms for data structures and algorithms C ++ and PHP, and data structures and algorithms RecursionRecursive Algorithm: it is an algorithm that calls itself directly or indirectly. Implementation process: You can use a function or sub-process to complete recursive operations by directly or indirectly calling the code in a function or sub-process. (

Eight sort algorithms that must be known [java Implementation] (3) Merge sort algorithms and Heap Sort Algorithms I. Merge Sorting Algorithm Basic Idea: The Merge Sorting method combines two (or more) ordered tables into a new ordered table, that is, the sequence to be sorted is divided into several subsequences, each of which is ordered. Then combine the ordered

convergence thresholds until convergence. The HITS algorithm can also be extended to other similar sorting systems. Hits variants Most of the problems encountered by the HITS algorithm are because hits is a purely link-based Analysis Algorithm without considering the text content. after kleberger proposed the HITS algorithm, many researchers improved hits and proposed many variant hits algorithms, mainly including: Improvement on hits by Monika R. he

Preface to the new book "algorithms-principles hidden behind data structures", data structures and algorithms Preface to the book "algorithms-principles hidden behind data structures" In the winter of 2014 AD, a biography of the legendary life of Alan Turing, the father of computer science, was released in the United States. This film is "Imitation Game". In the