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

1 Answers  

---

What are cases and variables?

0 Answers  

---

How to avoid overflow in the logistic function?

0 Answers  

---

What are neural networks? What are the types of neural networks?

0 Answers  

---

 A perceptron is: a) a single layer feed-forward neural network with pre-processing b) an auto-associative neural network c) a double layer auto-associative neural network d) a neural network that contains feedback

1 Answers  

---

What is the advantage of pooling layer in convolutional neural networks?

0 Answers  

---

How are artificial neural networks different from normal computers?

0 Answers  

---

What is artificial intelligence neural networks?

0 Answers  

---

 Which of the following is true? Single layer associative neural networks do not have the ability to: (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in a picture are connected or not a) (ii) and (iii) are true b) (ii) is true c) All of the mentioned d) None of the mentioned

1 Answers  

---

What is a neural network and what are some advantages and disadvantages of such a network?

0 Answers  

---

The network that involves backward links from output to the input and hidden layers is called as ____. a) Self organizing maps b) Perceptrons c) Recurrent neural network d) Multi layered perceptron

1 Answers  

---

How artificial neural networks can be applied in future?

0 Answers  

---