Independence Day Deal! Unlock 25% OFF Today – Limited-Time Offer - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions

CertNexus Exam AIP-210 Topic 7 Question 42 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 42
Topic #: 7
[All AIP-210 Questions]

Which of the following is NOT an activation function?

Show Suggested Answer Hide Answer
Suggested Answer: A

An activation function is a function that determines the output of a neuron in a neural network based on its input. An activation function can introduce non-linearity into a neural network, which allows it to model complex and non-linear relationships between inputs and outputs. Some of the common activation functions are:

Sigmoid: A sigmoid function is a function that maps any real value to a value between 0 and 1. It has an S-shaped curve and is often used for binary classification or probability estimation.

Hyperbolic tangent: A hyperbolic tangent function is a function that maps any real value to a value between -1 and 1. It has a similar shape to the sigmoid function but is symmetric around the origin. It is often used for regression or classification problems.

ReLU: A ReLU (rectified linear unit) function is a function that maps any negative value to 0 and any positive value to itself. It has a piecewise linear shape and is often used for hidden layers in deep neural networks.

Additive is not an activation function, but rather a term that describes a property of some functions. Additive functions are functions that satisfy the condition f(x+y) = f(x) + f(y) for any x and y. Additive functions are linear functions, which means they have a constant slope and do not introduce non-linearity.


Contribute your Thoughts:

Geoffrey
5 days ago
I'm going to go with B. Hyperbolic tangent. That's a classic activation function, so it can't be the one that's not an activation function.
upvoted 0 times
...
Fredric
18 days ago
I think the answer is A) Additive because it is not a commonly used activation function in neural networks.
upvoted 0 times
...
Selma
24 days ago
Haha, I bet the answer is C. ReLU. That's one of the most common activation functions, so it can't be the right answer here.
upvoted 0 times
Janna
9 days ago
C) ReLU
upvoted 0 times
...
Arthur
13 days ago
B) Hyperbolic tangent
upvoted 0 times
...
Rashad
15 days ago
A) Additive
upvoted 0 times
...
...
Vincenza
29 days ago
Ooh, this one's tricky! I think the answer is A. Additive, because that's just a linear operation, not an activation function.
upvoted 0 times
Charlesetta
18 hours ago
No, I'm pretty sure it's D) Sigmoid, that's an activation function.
upvoted 0 times
...
Nan
4 days ago
I think it's B) Hyperbolic tangent, because that is an activation function.
upvoted 0 times
...
Alecia
14 days ago
I agree, A) Additive is not an activation function.
upvoted 0 times
...
...
Maurine
1 months ago
D. Sigmoid is definitely an activation function, so that can't be the answer. Hmm, let me think...
upvoted 0 times
Brett
3 days ago
C) ReLU
upvoted 0 times
...
Rusty
6 days ago
B) Hyperbolic tangent
upvoted 0 times
...
Ryan
15 days ago
A) Additive
upvoted 0 times
...
...
Nichelle
1 months ago
But isn't hyperbolic tangent commonly used as an activation function in neural networks?
upvoted 0 times
...
Dan
1 months ago
I disagree, I believe the correct answer is B) Hyperbolic tangent.
upvoted 0 times
...
Nichelle
1 months ago
I think the answer is A) Additive.
upvoted 0 times
...
Kattie
2 months ago
I'm pretty sure the answer is A. Additive can't be an activation function, right?
upvoted 0 times
Vincenza
1 months ago
B) Hyperbolic tangent
upvoted 0 times
...
Brendan
1 months ago
No, that's incorrect. Additive is actually an activation function.
upvoted 0 times
...
Dorothea
1 months ago
A) Additive
upvoted 0 times
...
...

Save Cancel
az-700  pass4success  az-104  200-301  200-201  cissp  350-401  350-201  350-501  350-601  350-801  350-901  az-720  az-305  pl-300  

Warning: Cannot modify header information - headers already sent by (output started at /pass.php:70) in /pass.php on line 77