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A differentiable activation function makes the function computed by a neural network differentiable and the back propagation algorithm is applicable to it.

Correct Answer: True
A differentiable activation function makes the function computed by a neural network differentiable (assuming that the integration function at each node is just the sum of the inputs), since the network itself computes only the function compositions. And the error function also becomes differentiable.

The activation for the climb is equal to _____ where; Uj = Activation for nucleation of jog Uv = Activation for formation of the vacancy Um = Activation for formation of the self diffusion Ui = Activation for formation of the interstitial diffusion

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?

Arrange the following steps in sequence used in the course of training a neural network after initializing weight and biases. I. Backward propagation II. Forward propagation III. Compute the loss function IV. Repeat the steps for n iterations till we have minimized the loss function, without overfitting the train data

Which of the following are true? (i) A programming language option does not permit global variables of any king and has no nesting of procedures/functions, but permits recursion can be implemented with static storage allocation (ii) Multi-level access link (or display) arrangement is needed to arrange activation records-only if the programming language being implemented has nesting of procedures/function (iii) Recursion in programming languages cannot be implemented with dynamic storage allocation (iv) Nesting of procedures/functions and recursion require a dynamic heap allocation scheme and cannot be implemented with a stack-based allocation scheme for activation records (v) Languages which permit a function to return a function as its result cannot be implemented with a stack-based storage allocation scheme for activation records.

Mean Value theorem is applicable to the and continuous in open interval (a, b) b) Functions continuous in closed interval only and having same value at point ‘a’ and ‘b’ c) Functions continuous in closed interval and differentiable in open interval (a, b) d) Functions differentiable in open interval (a, b) only and having same value at point ‘a’ and ‘b’

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?

This unsupervised clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration.

This clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration Select one:

The rate expression for a heterogenous catalytic reaction is given by, - rA = K.KA PA(1 + KA.PA + Kr.PR), where K is surface reaction rate constant and KA and KR are absorption equilibrium constants of A and R respectively. If KR PR >> (1 + KA PA), the apparent activation energy EA is equal to (given E is the activation energy for the reaction and ΔHR and ΔHA are the activation energies of adsorption of R and A)

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?