I am interested in the field of Artificial Intelligence. I began by learning the various Machine Learning algorithms. The maths behind some were quite hard. For example back-propagation in Convolutional Neural Networks.
Then when getting to the implementation part, I learnt about TensorFlow, Keras, PyTorch, etc. If such frameworks are libraries providing much faster and more robust results, I wonder, will there be a necessity to code a neural network (say) from scratch using the knowledge of the maths behind back-prop, activation functions, dimensions of layers,etc?
Is it as if the only role of a Data-Scientist is to tune the hyper-parameters?
Further, as of now the field of AI does not seem to have any way to solve for these hyperparameters, and they are arrived at through trial and error. Which begs the question, can a hypothetical person with just basic intuition about what the algorithms do, be able to make a model just as good as a person who knows the detailed mathematics of these algos?