In artificial neural networks(ANN), the activation function helps us to determine the output of Neural Network. They decide whether the neuron should be activated or not. It determines the output of a model, its accuracy, and computational efficiency.
Inputs are fed into the neurons in the input layer. Inputs(Xi) are then multiplied with their weights(Wi) and add the bias gives the output(y=(Xi*Wi)+b) of the neuron. We apply our activation function on Y then it is transfer to the next layer.
Derivative or Differential: Change in y-axis w.r.t. change in x-axis.It is also known as slope.(Back prop)
Monotonic function: A function…
Data Scientist at Kushagramati. Keen interest in Machine Learning | Deep learning | NLP | Computer Vision practitioner.