Neural networks Flashcards

1
Q

What is a neural network?

A

A set of neurons connected by directed weighted edges

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

What is a neuron?

A

A fundamental processing element

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

What is a dendrite?

A

A set of inputs

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

What is an axon

A

A single output

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

What is a synapse?

A

A set of weights

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

How does a neural network work?

A

If the sum of inputs >= threshold decided, then the neuron is fired

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

What does a positive weight mean in a neural network?

A

Encourages a network to fire

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

What does a negative weight mean in a neural network?

A

Prevents a neuron from firing

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

What is an epoch?

A

The entire training set fed into the neural network

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

What is the training value?

A

The value that we require the network to product

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

What is the learning rate?

A

How fast the network converges to an output by adjusting the scale to correct the weights

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

What happens if the learning rate is too high?

A

Not able to predict accurately and skips the optimal solution

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

What happens if the learning rate is too low?

A

Not able to predict accurately and the training time increases

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

What do we do when the epoch produces an error?

A
  • Check the next inputs
  • Calculate the error by doing training value - output
  • If the error is not 0, then weight = weight + learningRateinputserror
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

What does it mean if a system is linearly separable?

A

The output data points can be separated using a linear boundary

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Explain what the values on a confusion matrix are

A
  • True positive
  • True negative
  • False negative
  • False positive
17
Q

What does it mean to be true negative?

A

Model predicts correctly for negative results

18
Q

What does it mean to be true positive?

A

Model predicts correctly for positive results

19
Q

What does it mean to be false negative?

A

Model predicts incorrectly for negative results

20
Q

What does it mean to be false positive?

A

Model predicts incorrectly for positive results

21
Q

How do we create XOR using other logic?

A

A XOR B = (A AND NOT B) OR (B AND NOT A)
- We have a hidden neural network layer