later but ADA is just a scalar number that determines how much of a step you want to take at each iteration how strongly or aggressively do you want to step towards that gradient in code the picture looks very similar so to implement gradient descent is just a few lines of code just like the pseudocode you can initialize your weights randomly in the first line you can compute your loss with respect to those gradients and with respect to those predictions and your data given that gradient you just update your weights in the opposite