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:
Both A and R are correct and R is the correct explanation of A.
Deep learning uses artificial neural networks with many hidden layers. Such a network is only useful once its weights are learned, and the algorithm that makes learning in many layers possible is backpropagation of errors combined with gradient descent.
Assertion A — Convolutional, recurrent and transformer models are all artificial neural networks with many layers of weighted neurons, so ANNs are the backbone of deep learning algorithms. A is correct.
Reason R — Backpropagation computes the gradient of the loss with respect to every weight by applying the chain rule layer by layer from the output back to the input; the weights are then updated by gradient descent. It is one of the standard techniques used to train an ANN. R is correct.
Link — Deep networks became the basis of deep learning because backpropagation lets the error signal reach and adjust the weights of every hidden layer, so the hierarchical features that make deep learning work can be learned. In this sense R explains why ANNs can serve as the backbone of deep learning.
The choices stating that R does not explain A, or that A or R is incorrect, do not match this reasoning.
Both A and R are correct and R is the correct explanation of A 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 :
What is the role of Back Propagation Algorithm ?
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 :
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
