Yeah no problem, this is even closer to my area of focus! What do you know about physics and thermodynamics?
I'd say a good intro for low background is from Tomczak[0]. He has a book, but the blog posts are nearly identical. He did a post doc with Max Welling (someone you should learn about if you want to get deep, like I was suggesting before). So I'd switch things up slightly. I'd go Intro -> Autoregressive -> Flow -> VAE -> Hierarchical VAE -> Energy Based Models -> Diffusion. It is worth learning about GANs btw, but this progression should be natural and build up.
Continuing from there, you're going to want to learn about things Langevin Dynamics, Score Matching, and so on. Start with Yang Song's blogs[1]. Your goal should be to understand this paper[2]. Once you get there, you should be able to understand the famous DDPM paper[3]. But why we went through Tomczak wasn't just to get a good understanding of diffusion at a deeper level, but because you need these tools to understand Stable Diffusion which really is just Latent Diffusion[4]. This should connect back with Tomczak's 2 Improving VAE papers and you should also be able to understand NVAE.
This is probably the quickest way to get you to a good understanding but if you want to dig deeper, which I highly encourage (because there are major issues that people aren't discussing) then you'll need more time. But you'll probably have to tools to do so if you go through this route. Other people I suggest looking into: Diederik Kingma, Ruiqi Gao, Stefano Ermon, Jonathan Ho, Ricky T. Q. Chen, and Arash Vahdat.
This post is a good example of the initial realization that folks experience with graphs as data structures. I can see author exclaiming, "Graphs are an intuitive way to express knowledge!" Graphs as data structures can be more nuanced than is usually discussed.
Hypernode graphs seem to be very applicable to knowledge graphs, though they have different names depending on the discipline -- nested state machines, statecharts [1], UML hierarchically nested states [2].
I recently participated in a lecture series on Applied Category Theory [3] that touched on the underlying math of composition. The course focused on optimization of design choices in complex decision spaces.
Its possible to do so, but it is difficult. A few years of experience in a successful systematic team is extremely helpful. Among many other things, you learn that running a profitable strategy involves the coordination of a number of different types of tasks, which are similar but different enough so that its difficult for one person to be simultaneously good enough at all of them.
To run a successful strategy requires strong signals (indicators that dictate what to buy/sell and when), execution (actual filling of orders on exchanges), risk management (which can include statistical risk modelling as well as draw-down controls), and infrastructure (the wrong type of bug can be costly enough to kill the whole operation!).
As mentioned earlier, there is overlap in the skills and experience required for these broad categories, but people in the quant hedge fund/asset management industry typically specialize in one. This is where larger shops have an advantage. The entire strategy is only as good as its weakest link. Its common for people who haven't worked in the space to focus mostly, or even exclusively, on the signals and infrastructure aspects.
If you were to group most of the successful quant funds out there by alpha time horizon you would see that funds within the same bucket are generally running very similar types of strategies using very similar signals (the general concepts behind successful signals/execution of varying time horizons are actually not that complicated, but just might take a while to explain). Again, that's not say its easy to do. With most of the equities and derivatives markets getting ever more efficient, tighter coordination and implementation of the entire strategy pipeline (signals, execution, risk mgmt, infrastructure) can lead to significant Sharpe/Information ratio improvements.
If you wanted to get a feel for how some successful people in the industry think about the problem, I would recommend reading through the following books in roughly the corresponding order:
I'd say a good intro for low background is from Tomczak[0]. He has a book, but the blog posts are nearly identical. He did a post doc with Max Welling (someone you should learn about if you want to get deep, like I was suggesting before). So I'd switch things up slightly. I'd go Intro -> Autoregressive -> Flow -> VAE -> Hierarchical VAE -> Energy Based Models -> Diffusion. It is worth learning about GANs btw, but this progression should be natural and build up.
Continuing from there, you're going to want to learn about things Langevin Dynamics, Score Matching, and so on. Start with Yang Song's blogs[1]. Your goal should be to understand this paper[2]. Once you get there, you should be able to understand the famous DDPM paper[3]. But why we went through Tomczak wasn't just to get a good understanding of diffusion at a deeper level, but because you need these tools to understand Stable Diffusion which really is just Latent Diffusion[4]. This should connect back with Tomczak's 2 Improving VAE papers and you should also be able to understand NVAE.
This is probably the quickest way to get you to a good understanding but if you want to dig deeper, which I highly encourage (because there are major issues that people aren't discussing) then you'll need more time. But you'll probably have to tools to do so if you go through this route. Other people I suggest looking into: Diederik Kingma, Ruiqi Gao, Stefano Ermon, Jonathan Ho, Ricky T. Q. Chen, and Arash Vahdat.
[0] https://jmtomczak.github.io/
[1] https://yang-song.net/
[2] Deep Unsupervised Learning using Nonequilibrium Thermodynamics https://arxiv.org/abs/1503.03585
[3] https://arxiv.org/abs/2006.11239
[4] High-Resolution Image Synthesis with Latent Diffusion Models https://arxiv.org/abs/2112.10752