I recently talked to a number of people who work in software and want to get to the point where they can read serious AI/ML papers like Denoising Diffusion Probabilistic Models.
But even though they did well in high school math, even AP Calculus, maybe even learned some undergraduate math...
the math in these cutting-edge AI/ML papers still looks like hieroglyphics.
So, how do you get from high school math to cutting-edge AI/ML?
Here’s a 4-stage roadmap.
• Stage 1: Foundational Math. All the high school and university-level math that underpins machine learning. All of algebra, a lot of single-variable calculus / linear algebra / probability / statistics, and a bit of multivariable calculus.
• Stage 2: Classical Machine Learning. Coding up streamlined versions of basic regression and classification models, all the way from linear regression to small multi-layer neural networks.
• Stage 3: Deep Learning. Multi-layer neural networks with many parameters, where the architecture of the network is tailored to the specific kind of task you’re trying to get the model to perform.
• Stage 4: Cutting-Edge Machine Learning. Transformers, LLMs, diffusion models, and all the crazy stuff that’s coming out now, that captured your interest to begin with.
Continue reading here for a deep dive into each stage, complete with a full description/rationale and with links to plenty of free resources that you can use to guide your learning.
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