27 Equations Every Data Scientist Needs to Know

Author(s): Julia

Originally published on Towards AI.

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Everybody’s talking about AI, but how many of those who claim to be “experts” can actually break down the math behind it? It’s easy to get lost in the buzzwords and headlines, but the truth is — without a solid understanding of the equations and theories driving these technologies, you’re only skimming the surface. Think you can just rely on the tools and libraries available today? Think again. If you want to truly innovate and stay ahead of the curve, you need to master the math that powers AI and data science. In this article, we’ll dive deep into the fundamental concepts that most people ignore — and why they’re absolutely crucial for anyone serious about working in AI.

Photo by ThisisEngineering on Unsplash

Gradient Descent is a fundamental optimization algorithm used in machine learning to minimize a function by iteratively moving in the direction of steepest descent. It’s particularly useful in training models with large datasets, as it efficiently finds the minimum of a cost function. The algorithm updates parameters in the opposite direction of the gradient of the function at the current point, with the size of the step… Read the full blog for free on Medium.

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Published via Towards AI