coursera robotics

Programming Question # #: Calculate the sum of squaresSource: Peking University Online Program evaluation system POJ (Coursera statement: The exercises completed on POJ will not count to the final results of Coursera. )Total time limit: 1000ms memory limit: 1024kBDescribeWrite the sum function so that it can calculate the sum of squares of the input series.#include InputThe first line is an integer t (T Eac

Programming Question # #: Calculates the sum of the lower 3 bits of an arraySource: Peking University Online Program evaluation system POJ (Coursera statement: The exercises completed on POJ will not count to the final results of Coursera. )Total time limit: 1000ms memory limit: 1024kBDescribeEnter an array of positive integers a[0], a[1], a[2], ..., a[n-1], and calculate the sum of their binary low 3-bit.#

Programming Question # #: Output 200Source: Peking University Online Program evaluation system POJ (Coursera statement: The exercises completed on POJ will not count to the final results of Coursera. )Total time limit: 1000ms memory limit: 1024kBDescribeMake the following code output a result of 200.#include InputNo input required.OutputThe output result is 200.Sample inputNo input required.Sample output200

Programming Question # #: Computes the sum of squares of integersSource: Peking University Online Program evaluation system POJ (Coursera statement: The exercises completed on POJ will not count to the final results of Coursera. )Total time limit: 1000ms memory limit: 1024kBDescribeThe following program reads an integer n each time, and exits if n is 0, otherwise outputs N and N squared.#include InputA k in

Programming Question # # #Source: POJ (Coursera statement: The exercises completed on POJ will not count against the final results of Coursera. )Note: Total time limit: 1000ms memory limit: 65536kBDescribeThe output of the following program is:5,55,5Please fill in the blanks:#include InputNoOutput5,55,5Sample inputNoSample output5,55,5TipsThe missing code has the following form: Big ________________{}

Simple implementation of Student information processing programSource: POJ (Coursera statement: The exercises completed on POJ will not count against the final results of Coursera. )Note: Total time limit: 1000ms memory limit: 65536kBDescribeIn a Student information processing program, it is required to implement a class that represents a student, and all member variables should be private.(Note: The evalua

Ng's machine learning courses on Coursera are the best choice for getting started. In addition, Yaser Abu-mostafa's machine learning program is more focused on theory, but also suitable for beginners. Learning deep learning does not require mastering machine learning, but it can be helpful if you have some understanding. In addition, Learning Classic machine learning, not just deep learning, can give us a deep theoretical background ———— because deep

Editor's note: Quora on the question: self-study machine learning technology, what advice do you have? (What is your recommendations for self-studying machine learning), Yann LeCun The answer under the question. This article by Lei Feng Net (public number: Lei Feng net) according to LeCun's reply collation, the original link: http://www.leiphone.com/news/201611/cWf2B23wdy6XLa21.htmlThere are a lot of materials, tutorials, and video courses on the Web for machine learning, including some universi

Knowledge points in this section: Bayesian Statistics and Normalization Online learning How to use machine learning algorithms to solve specific problems: setting up diagnostic methods to quickly identify problems Bayesian statistics and normalization (methods to prevent overfitting) is to find better estimation methods to reduce the occurrence of over-fitting conditions.In retrospect, the estimation method used in linear regression is least squares, and logistic regres

This article is from: Http://jmozah.github.io/links/Following is a growing list of some of the materials I found on the web for deep Learni ng Beginners. Free Online Books Deep learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville Neural Networks and deep learning by Michael Nielsen Deep Learning by Microsoft Deep learning Tutorial by LISA Lab, University of Montreal Courses Machine learning by Andrew Ng in Coursera