Study the scenario and answer the question. * 0.2 A WA,B=0.2 0.8 B AB=0.4 Assume that B is an intermediate node of a neural network, the forward propagation values of nodes A and B are 0.2 and 0.3; the associated error AB of node B that was computed by the back propagation algorithm is 0.4, the learning rate y is assume to be 0.5. How does the backpropagation algorithm update the weight WA, B of the edge that connects node A and B? What is the new value of weight WA,B?

Study the scenario and answer the question. * 0.2 A WA,B=0.2 0.8 B AB=0.4 Assume that B is an intermediate node of a neural network, the forward propagation values of nodes A and B are 0.2 and 0.3; the associated error AB of node B that was computed by the back propagation algorithm is 0.4, the learning rate y is assume to be 0.5. How does the backpropagation algorithm update the weight WA, B of the edge that connects node A and B? What is the new value of weight WA,B?

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

See similar textbooks

Related questions

Q: X2 W13.38 1.13, w23 -1.08 -.56 W24 XOR Gate WIL (14) -1.49- کہانیا -.46 -.23 output

A: This exercise is an introduction to using the FEED FORWARD and BACK PROPOGATION algorithm for the…

Q: Suppose that we use sigmod activation function and square loss function: (t- o)2. The learning rate…

A: In neural networks, gradient descent is used to train the network by minimizing the loss function.…

Q: giving the following weight matrix for a vector quantization neural network: w = [ 0.6000 0.6300…

A: Answer: I have given answered in the handwritten format

Q: A multi-layer neural network has its weight as below. All neurons use linear activation function.…

A: We need to calculate MSE loss of the neural network. In the below steps Mse calculation is given.

Q: 01/ Given the following description of a Perceptron: w1=6, w2=-3, b=-4 and the threshold function…

A: It is defined as the piece of a computing system designed to simulate the way the human brain…

Q: Design a one-layered Neural Network for these

A: ANSWER: Neural Network: Neural Network otherwise called neural nets is a sort of calculation in AI…

Q: Assume that you are using YOLO on a 15x15 grid, for a detection problem with 5 different classes…

A: Given Data : Grid Size = 15 x 15 Number of classes = 5 Number of anchor boxes per grid = 3

Q: Consider a Perceptron that has two input units and one output unit, which uses an LTU activation…

A: ANSWER:-

Q: By using neural network algorithm solve the XOR problem. Where X1 and X2 are inputs Y is the output.…

A: The XOr, or "exclusive or," difficulty is a well-known one in ANN research. The biggest problem with…

Q: Assume that the hidden layer uses ReLU, whereas the output layer uses sigmoid activation. Assume SSE…

A: The answer is

Q: The weights, activation function, and set of input values for a Neural Network are given below:…

A: Given Data : S1 S2 S3 x1 2 4 1 x2 3 2 3 x3 3 6 -2…

Q: Prove the updating equation for the Backpropagation:

A: Prove the updating equation for the Backpropagation:

Q: Please solve this question in machine learning quickly

A: The value of the output from of the second neuron in the hidden layer is , 0.662.

Q: ider the following neural network. Assume that outputs O1, are outputs of sigmoid activation…

Q: Assume that u(x,y)=x2+y2u(x,y)=x2+y2, y(r,t)=rsin(t)y(r,t)=rsin⁡(t) and…

A: Let's see the solution in the next steps

Q: A multi-layer neural network has its weight as below. All neurons use linear activation function.…

A: Given: A multi-layer neural network has its weight as below. All neurons use linear activation…

Q: Do learning occurs in feed forward and back propagation algorithms? Explain your answer for each of…

A: Introduction: Neural Network: A neural network is a method in artificial intelligence that teaches…

Q: )1. Consider the neural network in Figure 25.13. Let bias values be fixed at 0, and let the weight…

A: Here is the answer:-

Concept explainers

Use of XOR function

XOR function or exclusive OR function was introduced in Excel 2013. This function is available under excel logical functions. For the given statement, the XOR function will return true only when either of the statements is true. It returns false when both the statements are true or when both the statements are false.

Question

Assume DBSCAN is run for the following dataset with MINPOINTS-3 and
epsilon=5, what are the points that are not core or border points?
A dataset consisting of object A, B, C, D, E, F with the following distance matrix is
given:
distance
A
B
с
D
E
F
E and F
D and F
E and C
O A and F
A
0
B
1
0
C
2
3
0
D
4
8
11
0
E
6
9
12
14
0
F
7
10
13
15
16
0

Expert Solution

This question has been solved!

Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.

SEE SOLUTION Check out a sample Q&A here

Step 1: Question -1 :Introduction

VIEW

Step 2: Question -1 Correct Answer and Explanation

VIEW

Step 3: Incorrect Options and Explanation

VIEW

Solution

VIEW

Step by step

Solved in 4 steps

SEE SOLUTION Check out a sample Q&A here

Knowledge Booster

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.

Similar questions

7
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?
By using neural network algorithm solve the XOR problem. Where X1 and X2 are inputs Y is the output. Learning rate is 0.5. Threshold is 1 if weighted sum >= 0.5 otherwise 0 and weights w1 and w2 = 0.9 Add 17 with learning rate and w1, w2 before start solving the question. X1 X2 Y 0 0 0 0 1 1 1 0 1 1 1 0
Machine Learning Lab: Using linear activation function find out the output value(y) of the below listed neural network of 5 input with 1 output. Given threshold of y is 34. 1.
Program important
Q3. The weights, activation function, and set of input values for a Neural Network are given below: Weights: wl = 2, w2 = -3, w3 = 4, w4 = -2, w5 = 5 1 when p > 0 O when p = 0 -1 when p <0 Set of input values (Set S1, S2, S3 and input values x1, x2, x3, x4, x5): Activation function: f (p): where p is weighted sum of inputs S1 S2 S3 x1 2 4 x2 3 2 3 x3 3 6 -2 x4 -4 -3 3 X5 -3 -2 3 What is the final output value for each set of input values?
plz do
dont post unnecessary answer dont answer if you dont know else direct dislike dont post copied answer
1) design an artificial neural network with two inputs (X1 and X2). She won't have the bias. This network will have a hidden layer with two neurons (N1 and N2), plus an output layer, with one neuron (N3). The output of the network is called Y. Make the weights (W's) of each neuron explicit in the network, as well as the interconnections between all parts of the ANN.
find the new weights
Consider the following training set input values x' = (1, x? = (0, х3 (- 1, -0.5, -1)" No. desired output value 1. 0, - 1, 1)" - 1)" -1 2. 1 3. 1 Assume the learning rate is 0.1 and the initial weight vector is w, = (1, -1, 0)". After training draw the neural network.