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.
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Story provenance
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Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/why-gradient-descent-became-stochastic/ |
| Publication time | Fri, 29 May 2026 15:00:00 +0000 |
| Retrieval time | 2026-05-29T15:05:01.843Z |
| Last seen | 2026-05-29T15:05:01.843Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | ahky5VZW-Ifa |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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.