Working on data-science problems can be both exhilarating and frustrating. Exhilarating because the occasional insight that boosts your algorithm’s performance can leave you with a lasting high. Frustrating, because you’ll often find yourself at the dead-end of a one-way street, wondering what went wrong.

In this article, I’d like to recount five key lessons that I’ve learnt after one too many walks down dead alleyways. I’ve framed these as five questions that I’ve learned to ask myself before taking on new problems or approaches:

• Question #1: Never mind a neural network; can a human with no prior knowledge, educated on nothing but a diet of your training dataset, solve the problem?
• Question #2: Is your network looking at your data through the right lens?
• Question #3: Is your network learning the quirks in your training dataset, or is it learning to solve the problem at hand?
• Question #4: Does your network have siblings that can give it a leg-up (through pre-trained weights)?
• Question #5: Is your network incapable or just lazy? If it’s the latter, how do you force it to learn?

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