Showing posts with label neural networks. Show all posts
Showing posts with label neural networks. Show all posts

AI Terms Glossary - Active Learning

 



Active Learning is a suggested strategy for improving the accuracy of machine learning algorithms by enabling them to designate test zones.

The algorithm may choose a new point x at any time, examine the outcome, and add the new (x, y) pair to its training base.

Neural networks, prediction functions, and clustering functions have all benefited from it.




~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram


Be sure to refer to the complete & active AI Terms Glossary here.

You may also want to read more about Artificial Intelligence here.




AI Terms Glossary - Activation Functions

 


The use of activation functions rather than linear functions in traditional regression models gives neural networks a lot of their strength.

In most neural networks, the inputs to a node are weighted and then summed.

After that, a non-linear activation function is applied to the total.

Although output nodes should have activation functions suited to the distribution of the output variables, these functions are often sigmoidal (monotone rising) functions like a logistic or Gaussian function.

In statistical generalized linear models, activation functions are closely connected to link functions and have been extensively researched in that context.


See Also: 

Softmax.



~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram


Be sure to refer to the complete & active AI Terms Glossary here.

You may also want to read more about Artificial Intelligence here.




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