I’m slowly working my way through the FastAI deep learning course right now. In that course, everything is a nail and deep learning is a hammer — which makes sense, because it’s a course about deep learning.
As a total noob, I’m curious: how do you know when to pick a particular AI approach for a problem?
E.g. decision trees. Are they preferred when features must be well-defined? When data is smaller? When individual steps need to be observable? Etc.
The way I understand it as a quick rule of thumb is that if you have tabular-type data, try random forests first (decision tree ensemble) and then NN and see which gives you better results. If you have other type of data (images, sound, text, etc) NN is likely the way to go.
Thank you! That seems like a pretty rational starting point.
I’m enjoying what I’ve learned so far but the ecosystem is large and has its own technical language. It’s often difficult to dig up simple answers like this.
No problem! The fast.ai forums and discussion groups are really friendly and will help if you have questions.
I will admit that fast.ai goes a little ... fast ... but that is kind of their thing. If you do not have any math-heavy background and want to learn ML at a little deeper level there is a book titled "Grokking Machine Learning"
by Luis Serrano that I would recommend. The author is a good communicator and uses non-technical language to explain technical terms in a way that is easy to understand. I mostly already knew everything in the book when I read it, but it was so engaging that I read it anyway and did learn some new things.
As a total noob, I’m curious: how do you know when to pick a particular AI approach for a problem?
E.g. decision trees. Are they preferred when features must be well-defined? When data is smaller? When individual steps need to be observable? Etc.