I've been re-reading Jeremy Howard & Sylvain Gugger's Deep Learning for Coders with Fastai and PyTorch and I really appreciate the reminder that a lot of barriers to entry into the Deep Learning space can be productively put to one side.
Gatekeepers make four big claims:
- You need lots of maths to use Deep Learning to solve problems
- You need lots of data (think prodigious, Google-sized quantities) to use Deep Learning
- You need lots of expensive computers and custom hardware to use Deep Learning
- You need a PhD, preferably in Maths or Physics or some computation-heavy science
Needless to say, it's not that maths or more data or better hardware isn't maybe going to help or improve your experience. But to say that if you don't have those things then you shouldn't start is also (seemingly) inaccurate or not helpful.
If you are a domain expert in something that has nothing to do with Deep Learning or data science, you probably have a lot of problems that are like low-hanging fruit in terms of your ability to use powerful techniques like Deep Learning to solve them.