What papers or textbooks do i need to read to have all the basics / background knowledge to use pytorch and understand what I am doing based on solely the documentation pytorch provides?
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I mean that's a pretty massive undertaking.
If that's your goal, don't bother with pytorch at all.
Start by implementing the individual algorithms required to do a simple machine learning algorithm from scratch (only numpy).
You need to learn and be able to encode back propagation, Adam, sigmoid, etc.. I can't remember them all off hand but it's like maybe 4 or 5 different functions in total.
There are many tutorials for this. If you need me to, I can link you to some.
This is a great way to get the basics down, however, beware that things like pytorch are ultimately collaborative projects involving thousands of team members incorporating advancements and research from all kinds of sources.
Let me know if you need links or support. Most of it you can Google.
Honestly, I don't think that there's room for a competitor until a whole new paradigm is found. PyTorch's community is the biggest and still growing. With their recent focus on compilation, not only are TF and Jax losing any chance at having an advantage, but the barrier to entry for new competitors is becoming much higher. Compilation takes a LOT of development time to implement, and it's hard to ignore 50-200% performance boosts.
Community size tends to ultimately drive open source software adoption. You can see the same with the web frameworks - in the end, most people didn't learn React because it was the best available library, they learned it because the massive community had published so many tutorials and driven so many job adverts that it was a no-brainer to choose it over Angular, Vue, etc. Only the paradigm-shift libraries like Svelte and Htmx have had a chance at chipping away at React's dominance.