I am an ML researcher working in the industry:
by far the most effective way to maintain/advance my understanding of ML methods is implement the core of an interesting paper and reproduce (some) of their results. Completing a working implementation really forces your understanding to be on another level than if you just read the paper and think "I get it". It can be easy to read (for example) a diffusion/neural ode paper and come away thinking that you "get it" while still having a wildly inadequate understanding of how to actually get it to work yourself.
You can view this approach in the same way that a beginner learns to program. The best way to learn is by attempting to implement (as much on your own as possible) something that solves a problem you're interested in. This has been my approach from the start (for both programming and ML), and is also what I would recommend for a beginner. I've found that continuing this practice, even while working on AI systems professionally, has been critical to maintaining a robust understanding of the evolving field of ML.
The key is finding a good method/paper that meets all of the following
0) is inherently very interesting to you
1) you don't already have a robust understanding of the method
2) isn't so far above your head that you can't begin to grasp it
3) doesn't require access to datasets/compute resources you don't have
of course, finding such a method isn't always easy and often takes some searching.
I want to contrast this with other types of approaches to learning AI with include
- downloading and running other people's ML code (in a jupyter notebook or otherwise)
- watching lecture series / talks giving overviews of AI methods
- reading (without putting into action) the latest ML papers
all of which I have found to be significantly less impactful on my learning.
You can view this approach in the same way that a beginner learns to program. The best way to learn is by attempting to implement (as much on your own as possible) something that solves a problem you're interested in. This has been my approach from the start (for both programming and ML), and is also what I would recommend for a beginner. I've found that continuing this practice, even while working on AI systems professionally, has been critical to maintaining a robust understanding of the evolving field of ML.
The key is finding a good method/paper that meets all of the following
0) is inherently very interesting to you
1) you don't already have a robust understanding of the method
2) isn't so far above your head that you can't begin to grasp it
3) doesn't require access to datasets/compute resources you don't have
of course, finding such a method isn't always easy and often takes some searching.
I want to contrast this with other types of approaches to learning AI with include
- downloading and running other people's ML code (in a jupyter notebook or otherwise)
- watching lecture series / talks giving overviews of AI methods
- reading (without putting into action) the latest ML papers
all of which I have found to be significantly less impactful on my learning.