Why Gradient Descent Became Stochastic
The article explores the evolution of gradient descent into stochastic gradient descent within the context of machine learning. It provides a detailed explanation of linear regression and the mathematical principles behind it, including the derivation of formulas for slope and intercept. The author emphasizes the importance of understanding the underlying mathematics to fully grasp how these algorithms function.
- ▪The article discusses the transition from calculus-based optimization to Stochastic Gradient Descent.
- ▪It explains linear regression and the math behind calculating slope and intercept.
- ▪The author derives the formulas for slope and intercept using the Mean Squared Error loss function.
Opening excerpt (first ~120 words) tap to expand
Machine Learning Why Gradient Descent Became Stochastic A step-by-step journey from calculus-based optimization to Stochastic Gradient Descent Nikhil Dasari May 29, 2026 19 min read Share Photo by Sami TÜRK In this blog post, we are going to discuss not only how but also why gradient descent and stochastic gradient descent are used. We already know about linear regression, and recently I wrote about it in the context of vectors and projections. Now, we will try to understand gradient descent with the help of a linear regression problem. But before that, I just want to briefly recall what we already know about linear regression and the math behind it, so that anyone starting out finds it easy to follow.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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