27 Equations Every Data Scientist Needs to Know 0 like November 9, 2024Share this postAuthor(s): Julia Originally published on Towards AI. This member-only story is on us. Upgrade to access all of Medium.Everybodys talking about AI, but how many of those who claim to be experts can actually break down the math behind it? Its 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, youre 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, well dive deep into the fundamental concepts that most people ignore and why theyre absolutely crucial for anyone serious about working in AI.Photo by ThisisEngineering on UnsplashGradient Descent is a fundamental optimization algorithm used in machine learning to minimize a function by iteratively moving in the direction of steepest descent. Its 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.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post