Hello, as the title says, I was reading through the part 1of the course and i’ve reached the MNIST notebook. The only thing I cant quite grasp is why the bias is added to the weighted sum of each picture and not each pixel.
An example is:
(train_x*weights.T).sum() + bias
Why is this correct and not this:
(train_x*weights.T + bias).sum()
Lastly, I dont get how the dot product helps us calculate the weighted sum better than a for loop.
Also, I would like to get some further clarification as to why we choose the y=a*x + b function to predict images. Is that something standard ? Can we use others ?
Thanks in advance!