No. As a researcher, you can make it your goal to find/invent a smallest possible architecture for a given task (in terms of number parameters, or number of operations). Alternatively, you can try to invent an architecture to learn faster from data (or require less data to achieve state of the art results).

> As a researcher

But the post you replied to specifically said "as a hobbyist", so it doesn't really sound like there's much hope.

Hobbyist researcher? Seems like a more accessible plan anyway; fuck bothering with huge datasets and long training times and just focus on optimizing small architecture.

Yes. This is often, perhaps willfully given the incentives, overlooked by computer scientists (and people trained in that discipline). For a complete, cogent argument see:

http://static.googleusercontent.com/media/research.google.co...

Yes, you need data. But the amount depends on your task, and there are pretty significant sources of large amounts of data available online.

For example I was at a presentation where a person built a pretty interesting neural model based on 190k clinical records released via Kaggle. In most fields it is surprising how much data is easily accessible.

Data is cheap and all around us.

Processing power yes, but you can get started with a gaming pc.

If you want to play around, take a gander at Kaggle.