What is gradient descent and how can I visualize it?

What is gradient descent and how can I visualize it?

Gradient descent is an optimization algorithm used to minimize a function by iteratively moving towards the steepest descent. You can visualize it using contour plots or 3D surface plots to see how the algorithm converges to the minimum.

Can you show me an example of a contour plot for gradient descent?

Sure! In a contour plot, the x and y axes represent the parameters, and the contours represent the function values. As gradient descent progresses, you can see the path it takes towards the minimum.

What tools can I use to create these visualizations?

You can use tools like Matplotlib in Python, TensorFlow, or even online platforms like Desmos to create these visualizations.

Are there any common pitfalls when visualizing gradient descent?

Yes, common pitfalls include not scaling your axes properly, which can misrepresent the convergence, and using too few iterations, which may not show the full path to the minimum.