AI Neural Networks Interview Preparation Guide
Refine your AI Neural Networks interview skills with our 26 critical questions. Our questions cover a wide range of topics in AI Neural Networks to ensure youre well-prepared. Whether youre new to the field or have years of experience, these questions are designed to help you succeed. Download the free PDF to have all 26 questions at your fingertips. This resource is designed to boost your confidence and ensure youre interview-ready.26 AI Neural Networks Questions and Answers:
1 :: What is Artificial Intelligence Neural Networks?
For the sake of trying to produce intelligent behavior however really all that's being done is work with artificial neural networks where each cell is a very simple processor and the goal is to try and make them work together to solve some problem. That's all that gets covered in this book. Many people are skeptical that artificial neural networks can produce human levels of performance because they are so much simpler than the biological neural networks.
2 :: Suppose we need a MATLAB source code to recognize different regular geometric shapes
such as: squares,rectangles,triangles,circles and ellipses in different sizes using neural network.
All of the images containing these shapes should be in binary format with the size of 300*400 pixels.
Give us a MATLAB code to detect these
geometric shapes?
Explain your self
3 :: Explain Neural Networks?
A neural network can be defined as a model of reasioning
based on the human brain.
The human brain incorporates nearly 10 billion neurons and
60 trillion connections,Synapses,between them.
By using multiple neurons simultaneously,the brain can
perform its functions much faster than the faster computers.
Although a single neuron has a very simple structure,an
army of such elements constitutes a tremendous processing
power.
The network which represents the connections among several
neurons is called a neural network.
based on the human brain.
The human brain incorporates nearly 10 billion neurons and
60 trillion connections,Synapses,between them.
By using multiple neurons simultaneously,the brain can
perform its functions much faster than the faster computers.
Although a single neuron has a very simple structure,an
army of such elements constitutes a tremendous processing
power.
The network which represents the connections among several
neurons is called a neural network.
4 :: Explain Artificial Neural Networks (ANNs)?
A computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.
5 :: Why is the XOR problem exceptionally interesting to neural network researchers?
a) Because it can be expressed in a way that allows you to use a neural network
b) Because it is complex binary operation that cannot be solved using neural networks
c) Because it can be solved by a single layer perceptron
d) Because it is the simplest linearly inseparable problem that exists.
d) Because it is the simplest linearly inseparable problem that exists.
6 :: What is back propagation?
a) It is another name given to the curvy function in the perceptron
b) It is the transmission of error back through the network to adjust the inputs
c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.
d) None of the mentioned
c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.
Explanation:
Back propagation is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.
Explanation:
Back propagation is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.
7 :: Which of the following is not the promise of artificial neural network?
a) It can explain result
b) It can survive the failure of some nodes
c) It has inherent parallelism
d) It can handle noise
a) It can explain result
Explanation:
The artificial Neural Network (ANN) cannot explain result.
Explanation:
The artificial Neural Network (ANN) cannot explain result.
8 :: Why are linearly separable problems of interest of neural network researchers?
a) Because they are the only class of problem that network can solve successfully
b) Because they are the only class of problem that Perceptron can solve successfully
c) Because they are the only mathematical functions that are continue
d) Because they are the only mathematical functions you can draw
b) Because they are the only class of problem that Perceptron can solve successfully
Explanation:
Linearly separable problems of interest of neural network researchers because they are the only class of problem that Perceptron can solve successfully
Explanation:
Linearly separable problems of interest of neural network researchers because they are the only class of problem that Perceptron can solve successfully
9 :: A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0.
a) True
b) False
c) Sometimes - it can also output intermediate values as well
d) Can't say
a) True
Explanation:
Yes the perceptron works like that.
Explanation:
Yes the perceptron works like that.
10 :: Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.
a) True - this works always, and these multiple perceptrons learn to classify even complex problems.
b) False - perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do
c) True - perceptrons can do this but are unable to learn to do it - they have to be explicitly hand-coded
d) False - just having a single perceptron is enough
c) True - perceptrons can do this but are unable to learn to do it - they have to be explicitly hand-coded