# What is the difference between step size and learning rate in machine learning?

I am using TensorFlow to implement some basic ML code in Python. I was wondering if anyone could give me a short explanation of the meaning of and difference between step size and learning rate in the following functions.

I used tf.train.GradientDescentOptimizer() to set the parameter learning rate and linear_regressor.train() to set the number of steps. I've been looking through the documentation on tensorflow.org for these functions but I still do not have a complete grasp of the meaning of these parameters.

Thank you and let me know if there is any more info I can provide.

(I posted this on Stack Overflow before I knew there was a Data Science forum board too, sorry)

Both learning rate $$\eta$$ and step size $$\Delta w$$ are linked to gradient descent. In the most simple case they are linked by :
$$\Delta w = w(t+1)-w(t) = -\eta \frac{\partial E(w)}{\partial w}$$
Where $$t$$ is the epoch and $$E$$ the error function.
In that simple case, they only differ by $$- \frac{\partial E(w)}{\partial w}$$, which sometimes lead to use one term instead of the other. However in a more general case (learning rate depending on weights, learning rate depending on epoch, added momentum, or minibatch learning) the distinction may have a greater importance.