Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works
Deep Learning Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works From one gradient to every gradient Nikhil Dasari Aug 12, 2026 15 min read Share Photo by Phil S on Pexels Welcome back! First of all, thank you so much for the response to the first two parts of this series. It feels good that many of you have found them helpful.
- ▪Deep Learning Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works From one gradient to every gradient Nikhil Dasari Aug 12, 2026 15 min read Share Photo by Phil S on Pexels Welcome back!
- ▪First of all, thank you so much for the response to the first two parts of this series.
- ▪It feels good that many of you have found them helpful.
Towards Data Science files mainly under ai. We currently carry 139 of its stories.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/backpropagation-explained-for-beginners-part-3-how-backpropagation-really-works/ |
| Publication time | Wed, 12 Aug 2026 15:00:00 +0000 |
| Retrieval time | 2026-08-12T15:06:31.592Z |
| Last seen | 2026-08-12T15:06:31.592Z |
| 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 | bL-xrKoutO39 · 1 stories |
| 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
Deep Learning Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works From one gradient to every gradient Nikhil Dasari Aug 12, 2026 15 min read Share Photo by Phil S on Pexels Welcome back! First of all, thank you so much for the response to the first two parts of this series. It feels good that many of you have found them helpful. As always, if you have any thoughts, questions, or suggestions while reading, I’d love to hear your perspective. Now, let’s pick up where we left off in part 2. Why Recompute the Same Gradients? We calculated the gradient for w1w_1 using the chain rule.
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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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