Neural Networks Interview Questions - Quiz #45

Are you preparing for a job interview in the field of deep learning or neural networks? If so, you’re likely aware of how complex and technical these topics can be. In order to help you prepare, we’ve put together a list of common neural network interview questions and answers in form of multiple-choice quiz.

The quiz in this blog post covers basic concepts related to neural network layers, perceptron, multilayer perceptron, activation functions, feedforward networks, backpropagation, and more. We’ve included 15 multiple-choice questions, as well as 5 additional questions specifically focused on the backpropagation algorithm. I will be posting many more quizzes on the neural networks in time to come, including those related to detailed concepts on CNN, RNN, LSTM, Transformers, etc.

Some of the topics covered in the quiz include: the purpose of an activation function in a neural network, the difference between a multilayer perceptron and a convolutional neural network, the vanishing gradient problem, the meaning of weight initialization and the role of a bias term in a neural network.

We hope that this quiz will help you to prepare for your upcoming interview and improve your understanding of the key concepts related to neural networks and deep learning. Whether you’re a beginner or an experienced practitioner, it’s always helpful to review the basics and stay up-to-date on the latest industry developments. Good luck!

Table of Contents

Questions & Answers Practice Test on Neural Networks

#1. What is backpropagation?

A technique for updating the weights in a neural network

A method for generating new training examples

A type of regularization technique

A technique for initializing the weights in a neural network

#2. What is a neural network?

A programming language

A type of computer hardware

A type of database

A set of algorithms modeled after the human brain

#3. What is the chain rule in calculus, and how is it related to backpropagation?

The chain rule is used to compute the derivative of a composite function, and it is used in backpropagation to compute the gradient of the loss with respect to the output of the network

The chain rule is used to compute the derivative of a single function, and it is used in backpropagation to compute the gradient of the loss with respect to the output of the network

The chain rule is used to compute the derivative of a composite function, and it is used in backpropagation to compute the gradient of the loss with respect to the weights of the network

The chain rule is used to compute the derivative of a single function, and it is used in backpropagation to compute the gradient of the loss with respect to the weights of the network

#4. What is a convolutional neural network?

A type of unsupervised learning algorithm

A type of neural network designed for image data

A type of neural network designed for sequential data

A type of recurrent neural network

#5. What is weight initialization in a neural network?

The process of randomly setting the weights of the network

The process of selecting the activation function of the network

The process of optimizing the weights of the network

The process of regularizing the weights of the network

#6. What is the vanishing gradient problem?

The problem of the gradients in the network becoming too small

The problem of the gradients in the network becoming too large

The problem of the weights in the network becoming too small

The problem of the weights in the network becoming too large

#7. What is a bias term in a neural network?

A term that controls the amount of regularization applied to the network

A term that controls the learning rate of the network

A term that adds a constant value to the output of a neuron

A term that allows the network to shift the activation function

#8. What is a recurrent neural network?

A type of neural network designed for sequential data

A type of convolutional neural network

A type of unsupervised learning algorithm

A type of neural network designed for image data

#9. What is the purpose of an activation function in a neural network?

To introduce non-linearity into the output

To reduce overfitting

To increase the number of neurons in the network

To speed up training

#10. What is a feedforward network?

A type of neural network in which the output of one layer is fed back as input to the same layer

A type of neural network in which the output of one layer is fed as input to the next layer

A type of neural network in which the input is fed backwards through the layers

A type of neural network in which the weights are updated in a random fashion

#11. What is a learning rate in backpropagation, and how does it affect the training process?

The learning rate controls how quickly the weights of the network are updated during backpropagation, and it does not affect the training process

The learning rate controls how quickly the weights of the network are updated during backpropagation, and a high learning rate can lead to unstable training

The learning rate controls how quickly the weights of the network are updated during backpropagation, and a low learning rate can lead to unstable training

The learning rate controls how quickly the weights of the network are updated during backpropagation, and a high learning rate can lead to slower convergence

#12. What is a perceptron?

A type of regularization technique

A type of optimization algorithm

A type of neural network

A type of activation function

#13. What is backpropagation used for in neural networks?

To preprocess the input data

To regularize the network

To update the weights of the network

To initialize the weights of the network

#14. What is a multilayer perceptron?

A type of convolutional neural network

A neural network with multiple output layers

A type of unsupervised learning algorithm

A neural network with multiple hidden layers

#15. What is the gradient descent algorithm, and how is it used in backpropagation?

Gradient descent is used to update the weights of the network during backpropagation

Gradient descent is used to compute the gradient of the loss with respect to the weights of the network during backpropagation

Gradient descent is used to preprocess the input data during backpropagation

Gradient descent is not used in backpropagation

#16. What is the difference between batch gradient descent and stochastic gradient descent, and how are they used in backpropagation?

Batch gradient descent updates the weights of the network using a single training example at a time, while stochastic gradient descent updates the weights using the entire training dataset. Both methods are used in backpropagation to update the weights of the network.

Batch gradient descent updates the weights of the network using a small batch of training examples at a time, while stochastic gradient descent updates the weights using the entire training dataset. Both methods are used in backpropagation to update the weights of the network.

Batch gradient descent updates the weights of the network using the entire training dataset, while stochastic gradient descent updates the weights using a small batch of training examples at a time. Both methods are used in backpropagation to update the weights of the network.

Batch gradient descent updates the weights of the network using the entire training dataset, while stochastic gradient descent updates the weights using a single training example at a time. Both methods are used in backpropagation to update the weights of the network.

Finish

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin.
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Neural Networks Interview Questions – Quiz #45