Similarly, the tanh function is y = (e^x - e^-x) / (e^x + e^-x). It's derivative is (1 - y)(1 + y). Again, by an algebra coincidence, the derivative of the tanh function can be expressed in terms of ...

Confused about activation functions in neural networks? This video breaks down what they are, why they matter, and the most common types — including ReLU, Sigmoid, Tanh, and more! # ...

“In the early days of neural networks, sigmoid and tanh were the common activation functions with two important characteristics — they are smooth, differentiable functions with a range between [0,1] ...