The recent publication of Gato spurred a lot of discussion on wheter we may be witnessingth the first example of AGI. Regardless of this debate, Gato's makes use of recent developments in reinforcement learning, that is using supervised learning on reinforcement learning trajectories by exploiting the ability of transformer architectures to proficiently handle sequential data.
Reading the comments it seems that this point created some confusion to readers not familiar with these techniques. Some time ago I wrote an introductory article to how transformers can be used in reinforcement learning which may be helpful to clarify some of these doubts: https://lorenzopieri.com/rl_transformers/
Yea, what I meant is that the slides of Full Stack Deep Learning course materials provide a decent outline of all of the significant architectures worth learning.
I would personally not go to that low level of abstraction (e.g. implementing NNs in a new language) unless you really feel your understanding is shaky. Try building an actual side project, e.g. an object classifier for cars, and problems will arise naturally.