7- Neural networks always overperforms machine learning methods True False 8- in Neural Networks why use an activation function to: a. Limit the output of the network with in a certain range b. All of the above c. Solve the local minima problem d. Activate the output of the neurons 9- The use of nonlinear activation functions in the hidden layer doesn't increasing learning capacity. True False 10- Gradient descent is not as important parameter as the activation function during of neural network parameter learning True False 11- the role to the hidden layers a. To limit the output of the neurons between a certain range b. non of the above c. To learn features lime corners d. Speed up the learning process e. All of the obove

7- Neural networks always overperforms machine learning methods True False 8- in Neural Networks why use an activation function to: a. Limit the output of the network with in a certain range b. All of the above c. Solve the local minima problem d. Activate the output of the neurons 9- The use of nonlinear activation functions in the hidden layer doesn't increasing learning capacity. True False 10- Gradient descent is not as important parameter as the activation function during of neural network parameter learning True False 11- the role to the hidden layers a. To limit the output of the neurons between a certain range b. non of the above c. To learn features lime corners d. Speed up the learning process e. All of the obove

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Suppose we are fitting a neural network with three hidden layers to a training set. It is found that the cross validation error Jcv(0) is much larger than the training error Jtrain (0). Should we increase the number of hidden layers?
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E-learning https://elearning.usm.my Lab 8.pdf Lab 8.pdf 1 / 3 90% + an) and b = (b,, b2, b3, ..., b,) is defined 1. The dot product of two vectors a = (a1, a2, a3, ..., by a.b = Za,b, = a,b, +a,b, +..+a„b, . i=l The angle 0 between two vectors a and b is defined as a.b cos 0 = |a || b | where a.b is the dot product of the two vectors a and b, and | a| and | b| are the magnitudes of a and b where |c|=yc? +c3 +cị+..+c; if c= (c1, c2, c3, ...,Cn). Write a C++ program that reads in two vectors V1= (a1, a2, a3) and V2 = (b1, b2, b3) which computes the dot product and angle between these vectors. The program should contain the following functions: void main () – which reads in the vectors V1 and V2 and calls functions to compute the dot product of these two vectors, and compute the angle between these vectors. The vectors should be saved in arrays. void dot_product (double V1[], double V2[], double &dot_product) computes the dot product of V1 and V2 and returns the result via a reference…
{P. - 4 -1} 1. We have the following patterns: (P: input, t: output) Design a one-layered Neural Network for these patterns. (Remember OR, XOR,.) {P, - ).t = 0} %3D {P, - (;).+ - 0}
Answer this questions True or False : Q1:lf the learning rate is too small, then gradient descent may take a very long time to converge. Q2: If 00 and 01 are initialized at a local minimum, then one iteration will not change their values Q3: If 00 and 01 are initialized so that 00=01, then by symmetry (because we do simultaneous updates to the two parameters), after one iteration of gradient descent, we will still have 00=01
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In deep learning models, the activation function refers to a function in an artificial neuron (node) that delivers an output based on inputs. Activation functions in artificial neurons (nodes) are an important part of the role that the artificial neurons (nodes) play in modern artificial neural networks. One of the most popular activation functions for regression tasks is the rectified linear unit, or ReLU. Mathematically, the ReLU is defined as: х, x >0 0, x<0 Write a function called relu(x) that will perform the ReLU activation function on a given vector x. The function should return the output of the activation function. If X = -4:1:3, what is relu(X)?
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What is the diffrence between Random Forest and Ensemble Learning
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