The best laptop for hacking
The question comes up from time to time: what is the best laptop for hacking?
There was a time when I was a student when I was obsessed with more speed and more cores so that I could run my algorithms faster and longer. I changed my point of view. Big equipment still matters, but only after you've considered a lot of other factors.
The lesson is that if you're just starting out, your hardware doesn't matter. Focus on training with small datasets that fit in memory, such as those from the UCI Machine Learning Repository.
Learn good experimental design and make sure you ask the right questions and challenge your intuition by testing a variety of algorithms and interpreting your results through the lens of statistical hypothesis testing.
Once hardware starts to matter and you really need lots of cores and lots of RAM, rent it just in time for a carefully designed project or experiment.
More processor! More RAM!
I was naive when I first announced artificial intelligence and machine learning. I would take all available data and use it in my algorithms. I would rerun the models with small parameter tweets to improve the final score. I ran my models for whole days or weeks. I was obsessed
This mainly stemmed from the fact that I was interested in my machine learning skills at the competition. Obsession can be good, you can learn a lot very quickly. But if applied incorrectly, you can waste a lot of time.
I built my cars in those days. I would upgrade my CPU and RAM frequently. This was in the early 2000s, before multi-core was the clear path (for me), and even before GPUs, where there was a lot of talk about non-graphics usage (at least in my circles). I needed more and faster processors and I needed lots and lots of RAM. I even controlled the computers of my housemates so I could do more runs.
A little later, when I was in graduate school, I had access to a small cluster in the laboratory, and I began to use it. But things started to change, and it became less important how much computing power I had.