This is a good point, although I suppose you could still think of this in the framing of “just in time learning”, i.e. you can attempt a deep RL project, realise you are hopelessly out of your depth, then you know you’d better go through Spinning Up in Deep RL before you can continue.
Although the risk is that it may be demoralising to start something which is too far outside of your comfort zone.
you can attempt a deep RL project, realise you are hopelessly out of your depth, then you know you’d better go through Spinning Up in Deep RL before you can continue.
Tbc, I do generally like the idea of just in time learning. But:
You may not realize when you are hopelessly out of your depth (“doesn’t everyone say that ML is an art where you just turn knobs until things work?” or “how was I supposed to know that the algorithm was going to silently clip my rewards, making all of my reward shaping useless?”)
You may not know what you don’t know. In the example I gave you probably very well know that you don’t know RL, but you may not realize that you don’t know the right tooling to use (“what, there’s a Tensorboard dashboard I can use to visualize my training curves?”)
Both of these are often avoided by taking courses that (try to) present the details to you in the right order.
This is a good point, although I suppose you could still think of this in the framing of “just in time learning”, i.e. you can attempt a deep RL project, realise you are hopelessly out of your depth, then you know you’d better go through Spinning Up in Deep RL before you can continue.
Although the risk is that it may be demoralising to start something which is too far outside of your comfort zone.
Tbc, I do generally like the idea of just in time learning. But:
You may not realize when you are hopelessly out of your depth (“doesn’t everyone say that ML is an art where you just turn knobs until things work?” or “how was I supposed to know that the algorithm was going to silently clip my rewards, making all of my reward shaping useless?”)
You may not know what you don’t know. In the example I gave you probably very well know that you don’t know RL, but you may not realize that you don’t know the right tooling to use (“what, there’s a Tensorboard dashboard I can use to visualize my training curves?”)
Both of these are often avoided by taking courses that (try to) present the details to you in the right order.
I feel like I’m on both sides of this, so I’ll take the fast.ai course and then immediately jump into whatever seems interesting in PyTorch