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.
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)