Contents
What is derivative in activation function?
Why do we use the derivatives of activation functions in a neural network? Also, the derivative measures the steepness of the graph of a function at some particular point on the graph. In computational networks, the activation function of a node defines the output of that node given an input or set of inputs.
Why do we use partial derivative in gradient descent?
When there are multiple variables in the minimization objective, gradient descent defines a separate update rule for each variable. A partial derivative just means that we hold all of the other variables constant–to take the partial derivative with respect to θ1, we just treat θ2 as a constant.
How does the derivation of gradient descent work?
In Andrew Ng’s Machine Learning course, there is one small modification to this derivation. We multiply our MSE cost function by 1/2 so that when we take the derivative, the 2s cancel out. Multiplying the cost function by a scalar does not affect the location of its minimum, so we can get away with this.
How is gradient descent exhibited in backpropagation step?
The gradient descent process is exhibited in the form of the backpropagation step where we compute the error vectors δ backward, starting from the final layer. Depending upon the activation function, we identify how much change is required by much change is required by taking the partial derivative of the function with respect to w.
How to do gradient descent on MSE cost function?
Gradient Descent of MSE. Now that we know how to perform gradient descent on an equation with multiple variables, we can return to looking at gradient descent on our MSE cost function. The MSE cost function is labeled as equation [1.0] below. Taking the derivative of this equation is a little more tricky.
Is the learning rate for gradient descent too small?
The only concern with using too small of a learning rate is that you will need to run more iterations of gradient descent, increasing your training time. Note in the above example that gradient descent will never actually converge on the minimum, θ = 0 .