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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 wh
by mhuffman 4y ago
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.
- jnovek 4y agoThank 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.
- mhuffman 4y agoNo 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.