What is neural network topology? Explain any Two topologies in detail.

Explain briefly Mc Culloch Pitt’s (MP) artificial neuron model. Give its limitations.

Distinguish between Biological neural network and artificial neural networks.

Explain the structure and function of a biological neuron.

Obtain the output of the neuron Y for the network shown in figure using activation function as Binary sigmoidal.

What is a neural network activation function? Explain any one activation function in detail.

Write and explain perceptron learning algorithm.

Draw and explain the architecture of multilayer feed forward networks.

Differentiate between Learning and Memory.

Write and explain Hebbian learning Algorithm.

What is error correction learning? Explain in detail with diagram.

Differentiate between Feed Forward and Feedback neural network.

What is neural network topology? Explain any three topologies in detail.

Explain briefly McCulloch Pitt’s (MP) artificial neuron model. Give its limitations.

Distinguish between Biological Neural Network and Artificial Neural Networks.

Discuss briefly the structure and function of a biological neuron.

Obtain the output of the neuron Y for the network shown in figure using activation function as Binary Sigmoidal and Bipolar Sigmoidal.

Write and explain Hebbian learning Algorithm.

What is error correction learning? Explain in detail with diagram.

Differentiate between Feed Forward and Feedback neural network.

Draw the architecture of multilayer feed forward networks. Explain input layer, hidden layer & output layer computations in multilayer feed forward networks.

Explain perceptron learning algorithm and implement OR function using Perceptron network.

What is the role of activation function in neural network? Explain bipolar Sigmoid function in detail.

Why is ReLU the most commonly used Activation Function?

Explain architecture of Artificial Neural Network with a neat diagram.

Draw the structure of the biological neuron and explain working of the same in brief.

Write an algorithm of ADALINE and focus on its upper bound with largest Eigen Value of its correlation matrix.

What is Error Correction and how to minimize these errors?

Explain the architecture of Multilayered neural network.

Define learning and memory. Explain learning algorithms in details.

What is the difference between Forward propagation and Backward Propagation in Neural Networks?

Explain the different types of Gradient Descent in detail.

Write down Perceptron Learning Algorithm for OR function along with calculation of each input vector.

What is a neural network activation function? State its Types?

Explain architecture of Artificial Neural Network with a neat diagram.

Explain Mc-Culloch & Pitts model with an example.

What are the main differences among the three models of artificial neuron, namely, McCulloch-Pitts, Perceptron and Adaline?

Explain the structure and working of Biological Neural Network?

Differentiate between Biological Neural Network and Artificial Neural Network.

Explain Perceptron Learning Algorithm with an example.

Explain the architecture of Multilayered neural network.

Write and explain the steps of Back Propagation Learning algorithm.

Draw the architecture of Back Propagation Network and explain in detail.

Differentiate between Feed forward and Feedback neural network.

What is Error Correction and how to minimize these errors?

What is Hopfield Neural Network? What is a state transition Diagram for Hopfield Neural Network? Explain how to derive it in Hopfield model.

A hetero associative network is given. Find the weight matrix and test the network with the training input vectors. S1 = (1,1,0,0), S2 = (0,1,0,0), S3 = (0,0,1,1) S4 = (0,0,1,0) t1 = (1,0), t2 = (1,0), t3 = (0,1), t4 = (0,1)

Describe Boltzmann Machine and Boltzmann learning Law. What are the limitations of Boltzmann learning.

For a given input vector (S1,S2,S3,S4) and output vector T=(T1,T2). Find the weight matrix using hetero associative training algorithm. S1 = (1,1,0,0), S2 = (1,0,0,1), S3 = (1,1,0,0) S4 = (0,1,0,1) t1 = (0,1), t2 = (1,1), t3 = (1,0), t4 = (0,0)

Explain ART under the following headings : i) Architecture ii) Working iii) Training iv) Implementation

Describe the self-organization map (SOM) algorithm and explain how it can be used for feature mapping.

Discuss in detail the following with diagram: i) Learning vector quantization ii) Adaptive pattern classification

Draw the architecture of Kohonen Network and explain the algorithm for training the weights of the Network.

Consider a LeNet-5 a convolutional neural network, we want to perform the classification of digits, Write down the complete procedure followed in its architecture.

Draw and explain the architecture of Convolutional Neural Network.

Distinguish between CNN and RNN

Exemplify convolution over volume with convolution on RGB images. Also illustrate multiple filters used in it.

Explain the softmax regression with respect to hypothesis and cost function and write down its properties.

Distinguish between Pooling and Padding.

Discuss the following applications of ANN in detail. i) Recognition of consonant vowel (CV) segments. ii) Recognition of Olympic games symbols. iii) Recognition of handwritten characters

You have been asked to develop a model of recognizing hand written digits. What are the chosen steps for activity? Explain each with detail.

Write short note on the following. i) NET Talk ii) Texture classification iii) Pattern classification

Describe the Neocognitron model and its significance in the recognition of handwritten characters.

How does Hopefield network work and state its limitations.

Exemplify stimulated annealing with its advantages and disadvantages.

Define: i) Pattern association ii) Pattern classification iii) Pattern mapping tasks

Explain in detail stochastic gradient approach.

State basic functional units of ANN for pattern recognition tasks.

What is catastrophic forgetting in neural network?

Why Kohonens network are called self organizing maps?

What is Adaptive Resonance Theory and its applications?

Define following: i) Learning vector quantization ii) Adaptive pattern classification

How to recognize character using ART network?

What is competitive learning in neural network and its limitations?

Explain SOM architecture and its uses.

Why do we prefer Convolution Neural Networks(CNN) Over Artificial Neural Networks(ANN) for image data as input?

Write short note on: i) AlexNET ii) VGG-16 iii) Residual networks

Explain the role of the flattening layer in CNN.

What exactly is a CNN and how does it work?

Define bias and variance. What is bias-variance trade-off?

What do we use a pooling layer in a CNN?

Explain automatic language translation with its three basic rules.

Exemplify recognition of Olympic Games symbols.

What is NET talk?

Exemplify pattern classification?

Write a short note on: i) Texture classification ii) Texture segmentation

Illustrate about Neocognitron?

Draw the state transition diagram for the three types of Boltzmann Machine neural network and comment on the nodes and their connections.

Train the hetero-associative memory network using outer products rule to store input row vectors s = (s1, s2, s3, s4), to the output row vectors t = (t1, t2). Use the vector pairs as given in Table 2.

Discuss the False Minima problem, Stochastic Update and Simulated Annealing concepts with reference to the Boltzmann machines.

How do you define associative memory? What are the types of associative memories? Explain with 2 real applications of associative memories.

How Hebbian learning is different from competitive learning? What is winner- takes-it-all? Draw basic architecture of ART and discuss its principle of working.

Discuss the following features of ART: 1) self-scaling computational unit 2) self-adjusting memory search 3) Pre-learned pattern access and 4) Attention vigilance

Construct and test an LVQ net with five vectors assigned to two classes. The given vectors along with the classes are as shown in Table given below

Draw the architecture of Kohonen Network. Discuss the feature mapping concept. Explain the training algorithm of KSOFM.

Draw comparative architecture of ANN and CNN. What is the need for CNN? Will the overfitting be less in CNN than ANN? And if yes, why does this happen?

Illustrate (with diagram) the bias-variance dilemma? How the trade-off is achieved? Comment on: Bias Low with Variance High, Bias High with Variance Low

Discuss the relevance and significance of 1) pooling layer 2) padding 3) strided convolutions 4) dropout.

Compare and contrast: LeNet – 5, AlexNet, VGG –16

How neo-congnitron is different from MNIST handwritten recognition? Discuss in your own words with reference to the algorithmic steps.

How would you solve the problem of texture classification and segmentation? Develop your own block schematic for this task.

Develop your own NN model (algorithm) for Recognition of Olympic games symbols. Draw the architecture of your proposed system. Will you use backpropagation? Why?

How do we recognize consonant vowel (CV) segments? Is this signal different from Image classification project? Which NN algorithm would you use?

What is the Hopfield neural network? What is a state transition diagram for Hopfield Neural Network? Explain how to derive it in Hopfield model.

Explain the concept of associative learning in artificial neural networks. How is it related to pattern recognition?

Explain the architecture of Boltzmann machine.

Describe the Boltzmann machine and Boltzmann learning law. What are the limitations of the Boltzmann learning?

Write a short note on i) Stochastic Network ii) Simulated Annealing

Draw and explain Competitive learning Network.

Describe the self-organization map (SOM) algorithm and explain how it can be used for feature mapping.

Explain how ART can be used for character recognition task.

Explain briefly ART network. What are the features of ART network?

Describe the components of a competitive learning neural network and explain how they contribute to the network function.

What is vector quantization? How it is used for pattern clustering?

Explain the role of pooling layer in Convolution neural network.

Explain the concept of transfer learning and its importance in deep learning.

Explain Padding in neural network.

Explain Residual network in Convolution neural network.

Explain the concept of SoftMax regression and its significance in CNN models.

Explain how ANN can be used for the recognition of printed characters.

Describe the Neocognitron model and its significance in the recognition of handwritten characters.

Explain example of pattern recognition in everyday life.

Discuss the application of ANN in pattern classification and recognition of Olympic game symbols.

Explain texture classification and segmentation in ANN.

Discuss the application of ANN in the recognition of consonant vowel (CV) segments.

What do you understand by associative memory? Also mention characteristics and applications for the same.

Write short Notes on the following. i) State transition diagram ii) False minima problem

Illustrate the architecture of Boltzmann machine and its learning also its applications.

Explain Boltzmann machine. How does it differ from Hopfield net?

How does simulated annealing algorithm work?

Write short notes on the following. i) Applications of Hopfield Network for Travelling sales man problem ii) Associative Memory

What is competitive learning in neural networks?

Consider an ART-I network with input vector [1,1,0,0], [0,0,1,0], [1,1,1,0] and [1,1,1,1], want to produce clustering with following data, number of inputs n =4, clusters to be formed m = 3 and vigilance parameter ρ = 0.5 , Compute the result of the first iteration and comment on clustering.

Draw the network architecture of ART network. Explain the algorithm for designing the weights of ART network.

Explain ART under the following headings : i) Architecture ii) Working iii) Training iv) Implementation

Draw the architecture of Kohonen Network and explain the algorithm for training the weights of the Network.

Define following : i) Learning vector quantization ii) Adaptive pattern classification

Illustrate with example convolution and max pooling?

What frameworks are used in deep learning? Define any seven.

Explain the softmax regression with respect to hypothesis and cost function and write down its properties.

Exemplify convolution over volume with convolution on RGB images. Also illustrate multiple filters used in it.

Consider a LeNet-5 a convolutional neural network, we want to perform the classification of digits, Write down the complete procedure followed in its architecture.

What is transfer learning models for image classification? What are the 5 types of transfer learning?

Which device recognize a pattern of handwritten or printed characters? And also illustrate it’s working.

Explain texture classification using convolution neural network.

Write short notes on the following: i) NET Talk ii) Texture classification iii) Pattern classification

You have been asked to develop a model of recognizing hand written digits. What are the chosen steps for activity? Explain each with detail.

What is automatic translation? How does it work? What are its benefits?

What is neocognitron neural network and how it is trained?