Read the below passage and answer the questions . Artificial Neural Networks (ANNs) are computational models inspired by the human brain’s neural networks. They consist of inter-connected nodes, or neurons, organized into layers : an input layers, one or more hidden layers and an output layers. Each connection between neurons has a weight that adjusts as learning progresses allowing the network to adopt and improve its performance. ANNs are particularly effective in recognizing patterns making them valuable for tasks such as image and speech recognition, Natural language processing and predictive analytics. Learning in ANNs typically involves training algorithms like back propagation, which minimize the error by adjusting the weights. As a subset of machine learning, ANNs have revolutionized the field of Artificial Intelligence by providing solutions to complex problems that traditional algorithms struggle with.
What is the role of Back Propagation Algorithm ?
To reduce error
Back propagation is the standard supervised training algorithm for multilayer neural networks.
It runs the input forward to get the output, computes the error between the output and the target, then propagates that error backwards through the layers using the chain rule and adjusts every weight by gradient descent so that the error falls.
The passage says the same: training algorithms like back propagation “minimize the error by adjusting the weights”.
It has nothing to do with network security or the speed of data, and it does not add layers — the architecture is fixed before training; only the weights change.
To reduce error option is correct.
Similar Questions
Consider the two class classification task that consists of the following points :
Class \(C_{1}\) : [-1, -1], [-1, 1], [1, -1]
Class \(C_{2}\) : [1, 1]
The decision boundary between the two classes \(C_{1}\) and \(C_{2}\) using single perceptron is given by :
Consider the two class classification task that consists of the following points :
Class \(C_{1}\) : [1 1.5] [1 -1.5]
Class \(C_{2}\) : [-2 2.5] [-2 -2.5]
The decision boundary between the two classes using single perceptron is given by :
Perceptron learning, Delta learning and LMS learning are learning methods which falls under the category of
Which of the following is/are the application area(s) of ANN ?
(A) Natural Language Processing
(B) Image Processing
(C) Pattern Recognition
(D) Speech Recognition
Choose the correct answer from the options given below :
What is the role of weights in an ANN ?
Which of the following layers may be more than one in number ?
Artificial Neural Networks (ANNs) are inspired by :
Match List - I with List - II.
List - I List - II (A) The activation function (I) is called the delta rule. (B) The learning method of perceptron (II) is one of the key components of the perceptron as in the most common neural network architecture. (C) Areas of application of artificial neural network include (III) is always boolean like a switch. (D) The output of the perceptron (IV) system identification and control. Choose the correct answer from the options given below :
Given below are two statements: one is labelled as Assertion A and the other is labelled as Reason R.
A: Artificial Neural Networks are the backbone of deep learning algorithms.
R. Back propagation is one of the techniques used to train an ANN.
In the light of the above statements, choose the most appropriate answer from the options given below:
Which of the following is related to function of layers in a neural network?
A. Information →Input layer→ Hidden layer
B. Bias → output layer → hidden layer.
C. Hidden layer → algorithm → output layer.
D. Input layer → output layer → algorithms.
Choose the correct answer from the options given below:
Important Questions from Artificial Neural Network
In a feed forward neural network with the following specifications:
Input layer has 4 neurons, hidden layer has 3 neurons and output layer has 2 neurons using the sigmoid activation function for given input values [0.5, 0.8, 0.2, 0.6] as well as the initial weights for the connections.
W1: [0.1, 0.3, 0.5, 0.2]
W2: [0.2, 0.4, 0.6, 0.2] Input layer to hidden layer weights
W3: [0.3, 0.5, 0.7, 0.2]
W4: [0.4, 0.1, 0.3]
W5: [0.5, 0.2, 0.4] Hidden layer to output layer weights
What is the output of the output layer when the given input values are passed through neural network? Round the answer to two decimal places :
Given that $\eta$ refers to learning rate and $x_i$ refers to the $i^{th}$ input to the neuron, Which of the followings most suitably describes the weight updation rule of a Kohonen SOM? (where 'j' refers to the $j^{th}$ neuron in the lattice)
Consider the following table defining the sample inputs and corresponding target values for a perceptron model.
Sample No x1 x2 target w1 w2 S1 0 0 0 0 0 S2 0 1 1 S3 1 0 1 S4 1 1 1
What shall be the value of updated weights after applying all the samples S1 to S4 (in the order S1, S2, S3, S4) to this model. Given that the initial weights w1=0, w2=0, learning rate =0.1 and no bias is involved in the perceptron. The activation function for this perceptron is given below
$y_{observed} = \{ \begin{matrix} 1 & \text{if } y_{in} > 0 \\ 0 & \text{if } y_{in} < 0 \end{matrix}$In a multilayer perceptron, an error signal originates at an output neuron of the network and propagates ___________ through the network.
- Which of the following is true about discrete hopfield network ?
(i) may be modified to a continuous model
(ii) can be used for associative memory problems
(iii) nodes of the network have a continuous graded output
(iv) energy of the network increases continuously with time
