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.
How does ill-conditioning affect nn training?
What are artificial neural networks?
How artificial neurons learns?
How to avoid overflow in the logistic function?
What is a Neural Network?
How human brain works?
What is the role of activation functions in a Neural Network?
Who is concerned with nns?
What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ‘computational’ rates a) (i) and (ii) are true b) (i) and (iii) are true c) Only (i) d) All of the mentioned
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
What are the disadvantages of artificial neural networks?
What are the different layers in CNN?
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