gradient descent seems to fail

Question

I implemented a gradient descent algorithm to minimize a cost function in order to gain a hypothesis for determining whether an image has a good quality. I did that in Octav

Answer 1

If you remember the first Pdf file for Gradient Descent form machine Learning course, you would take care of learning rate. Here is the note from the mentioned pdf.

Implementation Note: If your learning rate is too large, J(theta) can di- verge and blow up', resulting in values which are too large for computer calculations. In these situations, Octave/MATLAB will tend to return NaNs. NaN stands fornot a number' and is often caused by undened operations that involve - infinity and +infinity.