I sometimes reminisce about the 2010-2020 deep learning and reinforcement learning etc era, as a student I did some projects in that domain back then and it felt very approachable and relatively easy to get into it compared to how I (most likely subjectively) see it today as a developer (in a different field).
I remember I doing a project in my second bachelor semester, where I generated random 64x64 images of a simple maze with a start and finish and then I tried to train a RNN algorithm that could navigate unseen mazes. There are so many better ways and algorithms to do it, but I learned a lot of cool tech anyway with this approach.
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[ 2.9 ms ] story [ 29.2 ms ] threadI remember I doing a project in my second bachelor semester, where I generated random 64x64 images of a simple maze with a start and finish and then I tried to train a RNN algorithm that could navigate unseen mazes. There are so many better ways and algorithms to do it, but I learned a lot of cool tech anyway with this approach.
only difference is u have to explain the difference between your ML thing and the gen. AI hypetrain bullshit, every time.
I seriously can't understand this take at all. Its like people going on and on about how much better horse drawn carriages are compared to cars.
- Reinforcement Learning Discussion on Discord, https://discord.com/invite/5nDB9dzZvp
- /r/reinforcementlearning, https://www.reddit.com/r/reinforcementlearning/
- CS285, Deep Reinforcement Learning course at Berkeley, https://rail.eecs.berkeley.edu/deeprlcourse/
And this was probably posted here before, but it's fun nonetheless: Gran Turismo played by an RL agent, https://www.gran-turismo.com/us/gran-turismo-sophy/
Edit: and there's this "blog" that gives some very practical advice for beginners: https://www.decisionsanddragons.com/