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You are the administrator of a Windows 2000 Server computer. The server contains one network adapter and is a file and print server for critical company resources. You install a second network adpater in the server and connect it to the same network subnet as the first adapter. You want to ensure that the first adapter is used for all network traffic and the second adapter is used only if the first adapter fails or is disconnected from the network. You also want to ensure that the server always has network connectivity even if one network adapter fails. What should you do?
A too-large weight initialized on the neural network can lead to vanishing gradient whereas a too-small weight initialized on the neural network can lead to exploding gradient.
Your network uses TCP/IP as the only network protocol. Devices on the network are configured to use IP address from the private 10.0.0.0 range. All the client computers on the network runs Windows 2000 Professional. The network includes Windows 2000 Server computers and UNIX servers. User's print jobs are sent to shared printers on a Windows 2000 Server computer named PrintServ that directs the print jobs to print devices attached directly to the network. You have a high-capacity print device that is attached to one of the UNIX servers. The UNIX computer uses the LPR printing protocol, and it's IP address is 10.1.1.99. The name of the printer queue is GIANT. You want users to be able to connect to this printer from their computers. What should you do?
Assume that you are given a data set and a neural network model trained on the data set. You are asked to build a decision tree model with the sole purpose of understanding/interpreting the built neural network model. In such a scenario, which among the following measures would you concentrate most on optimising?
Given above is a description of a neural network. When does a neural network model become a deep learning model?
Which of the following is true? (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works.
Which of the following is true?
(i) On average, neural networks have higher computational rates than conventional computers.
(ii) Neural networks learn by example.
(iii) Neural networks mimic the way the human brain works.
Mr. Justin wanted to test his students' understanding of the 'nature of weekly markets from a seller's point of view'. Which of the following questions are suited to test this? i. Can a weekly market address all the needs of a family? Why or why not? ii. Compare and contrast the characteristics of a weekly market and a permanent shop. iii. Are you likely to find a well-known brand as part of a weekly market? Why or why not? iv. Can any seller increase their price a lot compared to other sellers in a weekly market? Why or why not?
If the number of perfect square factors and perfect cube factors of 3000 is x and y respectively, then what is the ratio of the sum of total perfect square factors and the perfect cube factors to that of the total number of factors of 3000? Session Expired We will redirect you to login 1page in 5 secs. Please login again and resume your work
A perfect cube is an integer whose cube root is an integer. For example, 27, 64 and 125 are perfect cubes. If p and q are perfect cubes, which of the following will not necessarily be a perfect cube?