Comparison between Sigmoid and Softmax Activation Function with Python
An activation function is a function used in artificial neural networks which outputs a small value for small inputs, and a larger value if its inputs exceed a threshold.
If the inputs are large enough, the activation function "fires", otherwise it does nothing.
In other words, an activation function is like a gate that checks that an incoming value is greater than a critical number.
I compared Sigmoid and Softmax activation functions, then demonstrated the differences in Python.
You are welcome to provide your comments and subscribe to my YouTube channel.
The Python code is uploaded into https://github.com/AIMLModeling/Softmax
On this page of the site you can watch the video online Comparison between Sigmoid and Softmax Activation Function with Python with a duration of hours minute second in good quality, which was uploaded by the user Statistics and Risk Modeling 04 February 2023, share the link with friends and acquaintances, this video has already been watched 454 times on youtube and it was liked by 2 viewers. Enjoy your viewing!
