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# Backpropagation Explained for Beginners (Part 1): Building the Intuition

**[Towards Data Science](https://daily.dev/sources/tds)** · 13 min read · 0 upvotes · 0 comments

## Summary

A beginner-friendly introduction to backpropagation in neural networks, building intuition from first principles. Starting from a recap of forward propagation and a simple regression dataset, the post explains why neural networks need to learn, introduces the chain rule through simple examples, and derives the partial derivative of the MSE loss with respect to one weight (w1) step by step. The article sets up Part 2, which will cover the full backpropagation algorithm as a more efficient way to compute all gradients.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/backpropagation-explained-for-beginners-part-1-building-the-intuition>

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---

Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#neural-networks](https://daily.dev/tags/neural-networks), [#gradient-descent](https://daily.dev/tags/gradient-descent)

[View this post on daily.dev](https://daily.dev/posts/backpropagation-explained-for-beginners-part-1-building-the-intuition-219g21wnz)

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