20 Artificial Neural Network Interview Questions and Answers

An Artificial Neural Network (ANN) is a computational model that is inspired by the biological neural networks that make up the brain. These networks are used to approximate complex functions that are difficult to express in traditional mathematical notation. They are commonly used in fields such as image recognition, pattern recognition, and anomaly detection.

If you are interviewing for a position that involves working with ANNs, it is important to be prepared to answer questions about your experience and knowledge. In this article, we will review some common ANN interview questions and provide some tips on how to answer them.

Artificial Neural Network Interview Questions and Answers

Here are 20 commonly asked Artificial Neural Network interview questions and answers to prepare you for your interview:

1. What is an artificial neural network?

An artificial neural network is a computer program that is designed to simulate the workings of a human brain. Neural networks are used to recognize patterns, make predictions, and learn from data.

2. Can you explain what a neuron in the context of ANNs is?

A neuron is the basic unit of an artificial neural network. It is a mathematical function that takes in a set of input values and produces an output value. The output value is determined by the weights of the inputs and the threshold value of the neuron.

3. How are neurons organized in an artificial neural network?

There are three main types of neural networks: feedforward, recurrent, and convolutional. In a feedforward neural network, neurons are arranged in layers, with each layer fully connected to the next. In a recurrent neural network, neurons are arranged in a loop, so that information can flow back through the network. In a convolutional neural network, neurons are arranged in a series of layers, with each layer connected to a subset of the neurons in the previous layer.

4. Why do we use more than one layer in ANNs?

The main reason we use more than one layer in ANNs is because it allows the network to learn more complex patterns. By having multiple layers, the network can learn to recognize patterns that are not linearly separable, which is a major limitation of single-layer networks. Additionally, multiple layers allow for a more efficient use of resources, as each layer can specialize in learning different types of patterns.

5. Can you explain the concept of back-propagation?

Back-propagation is a method used to train artificial neural networks. It involves adjusting the weights of the connections between the neurons in the network according to the error in the output of the network. This is done by propagating the error backwards through the network, starting from the output layer and working towards the input layer.

6. What’s the difference between deep learning and machine learning?

Deep learning is a subset of machine learning that is concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. These algorithms are used to learn high-level abstractions in data by using a deep graph with many processing layers, as opposed to shallow machine learning algorithms that only contain a few layers.

7. What are some common applications of ANNs?

ANNs are commonly used for tasks such as image recognition, pattern recognition, and data classification.

8. What is supervised learning?

Supervised learning is a type of machine learning algorithm that is used to learn from labeled training data. The goal of supervised learning is to build a model that can make predictions about new data points.

9. What is unsupervised learning?

Unsupervised learning is a type of machine learning algorithm that is used to find patterns in data. Unlike supervised learning algorithms, which are given training data that is labeled with the correct answers, unsupervised learning algorithms are given data that is not labeled. They must then find the patterns in the data on their own.

10. Can you give me some examples of where ANNs can be used to solve real problems?

ANNs can be used for a variety of tasks, including but not limited to:

– Pattern recognition
– Data classification
– Data clustering
– Function approximation
– Time series prediction

11. What are the advantages of using ANNs for modeling data?

ANNs are well-suited for modeling data because they are able to learn complex patterns and relationships. Additionally, ANNs are flexible and can be adapted to different types of data and different problem domains.

12. What are the disadvantages of using ANNs for modeling data?

There are a few disadvantages to using artificial neural networks for modeling data. One is that they can be computationally intensive, so if you are working with large datasets it can take a long time to train the network. Additionally, ANNs can be difficult to interpret, so if you are trying to understand how the network is making predictions it can be difficult to understand the underlying logic. Finally, ANNs can be prone to overfitting, so if the training data is not representative of the real-world data the network will be trying to predict, it can perform poorly.

13. What are the different types of activation functions available for training an ANN? Which activation function would you recommend for certain situations?

The different types of activation functions available for training an ANN are linear, threshold, sigmoid, and rectified linear. Each has its own advantages and disadvantages, so it is important to select the right activation function for the task at hand. For example, linear activation functions are good for simple tasks, while sigmoid activation functions are better for more complex tasks.

14. What are the different layers that make up an ANN?

The different layers that make up an ANN are the input layer, the hidden layer, and the output layer. The input layer is responsible for receiving input from the outside world. The hidden layer is responsible for processing that input and generating output. The output layer is responsible for taking that output and generating a response.

15. Can you explain how stochastic gradient descent works when training an ANN?

When training an artificial neural network, stochastic gradient descent is an algorithm that is used to minimize the error function. This function is used to calculate the error between the predicted output and the actual output. The algorithm works by taking small steps in the direction that will reduce the error function.

16. What are the steps involved in creating a perceptron?

The steps involved in creating a perceptron are as follows:

1. Initialize the weights and bias to small random values.

2. For each training example:

– Calculate the output of the perceptron using the current weights and bias.

– Update the weights and bias based on the error.

3. Repeat step 2 until the error is minimized.

17. What are the major differences between linear and nonlinear classification?

Linear classification is a method of classification that is based on a linear decision boundary, meaning that the boundary between classes is a straight line. Nonlinear classification is a method of classification that is based on a nonlinear decision boundary, meaning that the boundary between classes is not a straight line.

18. What does it mean for an ANN to learn?

Learning in an artificial neural network refers to the process by which the network adjusts its weights and biases in order to better approximate the desired output. This process is typically done through some form of gradient descent, where the error is backpropagated through the network in order to update the weights and biases in a way that reduces the error.

19. Can you explain the basic structure of a cell in an ANN?

The basic structure of a cell in an ANN is a neuron. A neuron is made up of a cell body, dendrites, and an axon. The cell body contains the nucleus of the cell, which houses the DNA. The dendrites are the branching structures that extend from the cell body and receive input from other neurons. The axon is the long, thin structure that extends from the cell body and transmits output to other neurons.

20. What are the five most important aspects of any robust AI system?

There are five key aspects to any robust AI system:

1. Machine learning: This is the ability of the system to learn from data and improve its performance over time.

2. Natural language processing: This is the ability of the system to understand human language and respond in a way that is natural for humans.

3. Robotics: This is the ability of the system to control and interact with physical objects.

4. Computer vision: This is the ability of the system to see and interpret the world around it.

5. Planning and decision making: This is the ability of the system to make decisions based on its goals and the current situation.