Pay attention to the timing of your edit/compile/test cycle time. Efforts to get this shorter pay off both in more iterations and in your personal motivation (interacting with a more-responsive system is more rewarding). Definitely try to get it under a minute.
A good dataset is incredibly valuable. When starting to attack a problem—both the whole thing, and subproblems that will arise—build a dataset first. This would be necessary if you are doing any machine learning, but it is still incredibly helpful even if you personally are doing the learning.
Succeed “instantaneously”—and don’t break it. Make getting to “victory”—a complete entry—your first priority and aim to be done with it in a day or a week. Often, there’s temptation to do a lot of “foundational” work before getting something complete working, or a “big refactoring” that will break lots of things for a while. Do something (continuous integration or nightly build-and-test) to make sure that you’re not breaking it.
I have some advice.
Pay attention to the timing of your edit/compile/test cycle time. Efforts to get this shorter pay off both in more iterations and in your personal motivation (interacting with a more-responsive system is more rewarding). Definitely try to get it under a minute.
A good dataset is incredibly valuable. When starting to attack a problem—both the whole thing, and subproblems that will arise—build a dataset first. This would be necessary if you are doing any machine learning, but it is still incredibly helpful even if you personally are doing the learning.
Succeed “instantaneously”—and don’t break it. Make getting to “victory”—a complete entry—your first priority and aim to be done with it in a day or a week. Often, there’s temptation to do a lot of “foundational” work before getting something complete working, or a “big refactoring” that will break lots of things for a while. Do something (continuous integration or nightly build-and-test) to make sure that you’re not breaking it.