CNN architecture for arc fault detection - How deep to learn new methods

You said: In terms of developing AI soultions as AI eng, we need to learn the new method/solution, e.g CNN, transformer, to what extent of understanding, we can hands on…

In terms of developing AI soultions as AI eng, we need to learn the new method/solution, e.g CNN, transformer, to what extent of understanding, we can hands on to apply, or what is your suggested learning way, without dropping to the rabbit holes of understanding questions

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Claude responded: You need enough understanding to make good decisions and debug, not enough to re-derive the math.

You need enough understanding to make good decisions and debug, not enough to re-derive the math. Ask yourself whether you can answer three things about a method:

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\n1. What problem does it solve, and what assumption does it make? CNNs assume nearby samples are related and patterns repeat anywhere (local + shift-invariant). Transformers let every position look at every other position (global context), at a cost that grows with sequence length squared.
\n2. What are the knobs, and what does each trade off? Kernel size, stride, depth, channels for CNNs. Sequence length, heads, embedding size for transformers. You should know which knob to turn when accuracy, memory, or latency is the problem.
\n3. How does it fail? Overfitting on small data, aliasing from stride, transformers needing lots of data, quantization accuracy loss.
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If you can answer those, you can apply it. Backprop derivations and proofs are optional unless you’re doing research.

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A learning loop that avoids rabbit holes:

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\n- Get it running first. Take a working example (like the notebook) and run it before reading theory deeply.
\n- Break it on purpose. Change one thing, predict what will happen, then check. This builds intuition faster than reading, and it’s what the exercises in the notebook are for.
\n- Learn theory on demand. Only dig deeper when a result surprises you or you’re stuck. Then the question is concrete, so you know when to stop.
\n- Timebox curiosity. If a \“why\” question isn’t changing a decision you’ll make, note it in a \“later\” list and move on. Most of those questions get answered naturally over time.
\n- Apply it to your domain. One real AFCI dataset teaches more than five tutorials on cats vs dogs.
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When to go deeper: when you need to modify an architecture, when standard approaches fail on your data, when you’re squeezing a model onto an MCU (then quantization and operator costs matter in detail), or when you need to defend design choices in a review or paper.

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For transformers specifically, a good next step after this notebook is to replace the Conv1D layers with a small attention layer on the same synthetic data, compare accuracy and size, and see firsthand why CNNs usually win on tiny embedded signal tasks.

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