Thanks that’s really helpful.
I am a complete beginner to this but I have completed the MLS with 100% in all grades exercises and I am part through the DLS.
My aim is to complete the DLS then demonstrate my knowledge from these courses by building an image classifier NN which is trained from a small input dataset of 2000 plant leaf pictures for 5 different plant species and see if I can train it to predict one of those plant leaf species and use this exercise to demonstrate to employers a real- world example of multi class image classification.
Is 2000 images as an input training dataset large enough? I will actually be using 1000 original different plant leaf images but doubling the size of the total input training dataset by applying data augmentation by flipping each image from left to right.
Should I also perform z-score normalisation on each input pixel feature for every image?