[time_to_smile]

I'm a huge fan of classical AI, and adore PAIP, but this isn't really true if your goal is anything other than a deep understanding of AI in the most general sense.

While it would be great if everyone interested in the topic was well versed in the fundamentals, the truth is if you want to do anything from building something cool over the weekend to getting an actual job doing AI work, you're much better off starting not only with ML, but specifically with current SotA neural networks.

If you really want to get started in AI I highly recommend building even a trivial implementation of Stable Diffusion on your own. Not just because it's cool, but because at its heart it is an excellent demonstration of how current differentiable programming works. Diffusion models involve chaining together 3 separate models into an entire system that learns to solve a complex task. Once you understand this deeply, you can now solve a very broad range of tricky problems and are really approaching what we think of when we think of AI.

Differentiable programming is really the current pathway to any sort of AI solution to a problem.

I say this as the token "have you tried logistic regression?" guy in my org.

[freeone3000]

The downside of differential programming is the absolutely massive amounts of training data and time required. Several orders of magnitude over boosted decision trees or even SVMs. If your function’s domain is fairly well understood, save yourself a few weeks and a few thousand dollars.

[time_to_smile]

You can implement SVM, gradient boosted decisions trees, and almost all classical models using the techniques of differentiable programming and it will have 0 impact on the amount of data required.

Massive Neural Nets do require a lot of data and are often not the best solution, but differentiable programming in general does not have higher data requirements than manually computing your derivatives or using OLS. You can still approach classical ML from the perspective of differentiable programming (and likely end up with a better sense of our how your models work in the end).

[anlunx]

Any recommendation for learning how to build a diffusion model?