coursera employees

Questions -31 point Possible (graded) Total time limit: 1000ms Memory Limit: 65536kB Describe Write a two-dimensional array class Array2, so that the following program output is: 0,1,2,3, 4,5,6,7, 8,9,10,11, Next 0,1,2,3, 4,5,6,7, 8,9,10,11, Program: #include Add your code here int main () { Array2 a (3,4); int i,j; fo

)/m; at End - End - -%size (J,1) -%size (J,2) - ind3 = A3-Ty; -D2 = (D3 * THETA2 (:,2: End)). *sigmoidgradient (z2); toTheta1_grad = Theta1_grad + d2'*a1/m; +Theta2_grad = Theta2_grad + d3'*a2/m; - the% ------------------------------------------------------------- *jj=0; $ Panax Notoginseng forI=1: Size (Theta1,1) - forj=2: Size (Theta1,2) theJJ = JJ + Theta1 (i,j) *theta1 (i,j) *lambda/(m*2); + End A End theSize (Theta1,1); +Size (Theta1,2); - $ forI=1: Size (THETA2,1) $

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

Someting about Lists mutation1 ###################################2 #Mutation vs. Assignment3 4 5 ################6 #Look alike, but different7 8A = [4, 5, 6]9b = [4, 5, 6]Ten Print "Original A and B:", A, b One Print "is they same thing?"+ F isb A -A[1] = 20 - Print "New A and B:", A, b the Print - - ################ - #aliased + -c = [4, 5, 6] +D =C A Print "Original C and D:", C, D at Print "is they same thing?"+ D isD - -C[1] = 20 - Print "New C and D:", C, D - Print - in ##############

points of mini project are translated, Then translate the Mini project implementation steps, not a one-time full translation, take too long, the previous translation may forget, and the translation may not be accurate, and sometimes to see the original text. Complete a paragraph and translate the next paragraph, step by step. Do not translate all, some do not help to complete the task can not translate, save time. 4. Selective translation of code clinic,5. If you get stuck, search for keywords

Week 2 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 Suppose a query has a total of 5 relevant documents in a collection of documents. System A and System B have each retrieved, and the relevance status of the ranked lists is shown below: Sys

Week 4 Practice quizhelp Center The 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 Can a crawler that only follows hyperlinks identify hidden pages, does not have any incoming links? No Yes question 2 after obtaining the chunk's handle and locations from th

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 Character classification Using s

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 said to learn from experience E with R Espect to some class of tasks T and performance measure P, if it performance at tasks in T, as measured By P, improves with experience E."Examp

Operating system Learning notes----process/threading Model----Coursera Course note process/threading model 0. Overview 0.1 Process ModelMulti-Channel program designConcept of process, Process control blockProcess status and transitions, process queuesProcess Control----process creation, revocation, blocking, wake-up 、...0.2 threading ModelWhy threading is introducedThe composition of the threadImplementation of threading mechanismUser-level threads, c

full implementation of multi-layered neural network recognition picture of the cat Original Coursera Course homepage, in the NetEase cloud classroom also has the curriculum resources but no programming practice. This program uses the functions completed in the last job, fully implementing a multilayer neural network, and training to identify whether there is a cat in the picture. There is no comment in the Code and Training test data download Cod

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 consider each English word, or easy to make mistakes;(3) I am using MATLAB to submit the programming job, because of the MATLAB command is

Took a course on software security at Coursera. Here is a list of readings from the professor:Week 1ReadingsRequired ReadingThe only required reading this week is the following: Common Vulnerabilities Guide for C programmers. Take note of the unsafe C library functions listed here, and how they is the source of the buffer overflow vulnerabilities. This list is relevant for the project and this week ' s quiz. (Reference) Memory layout. Exp

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

[ZT] How should the boss promise to core employees? How should the boss promise to core employees?Author: Jing suqi 10:18:11Source: blog China (blogchina.com) Source: Chinese and Foreign Management b28123cThe flow of employees is normal, but the flow is also regular.Generally, most of the employees who left the compan

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