The 2 opponents walk into the ring, each claims to have the upper hand. The info scientist pulls out a silver ruler, the deep learning developer pulls out a gleaming hammer — who will construct the perfect model?
In my previous positions, I’ve worked as each a knowledge scientist and a deep learning algorithm developer. Should you ask me what the differences are between the 2, I’ve got to say that it’s not clear-cut.
Each cope with data and machine learning models, and each use similar success metrics and dealing principles.
So what makes them different?
I believe its the attitude.
I’ll be daring and generalize that from my experience, deep learning developers (especially junior ones) are inclined to focus more on the model, while data scientists do the other — they analyze and manipulate the information such that just about any model will do the trick.
Or, should I dare to simplify it even further and say that:
Deep Learning = Model Oriented
