Data Science PR
Activation Function in Data Science

What is an Activation Function in Data Science?

Neural network activation functions are a crucial component of deep learning. 

Activation functions determine the output of a deep learning model, its accuracy, and also the computational efficiency of training a model – which can make or break a large scale neural network. Activation functions also have a major effect on the neural network’s ability to converge and the convergence speed, or in some cases, activation functions might prevent neural networks from converging in the first place.

In neural networks, linear and non-linear activation functions produce output decision boundaries by combining the network’s weighted inputs. The ReLU activation function is the most commonly used activation function right now, although the Tanh or hyperbolic tangent, and Sigmoid or logistic activation functions are also used.

Data Science PR

0 0 vote
Article Rating
Notify of

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Inline Feedbacks
View all comments
  • new-year-res-google-ads-v2_28-courses_300x600.jpg
  • 44406_22.jpg

Follow us

Don't be shy, get in touch. We love meeting interesting people and making new friends.

Would love your thoughts, please comment.x