--- title: Implemented GMM, GBM & MLP from scratch. | daily.dev description: A developer shares from-scratch implementations of Gaussian Mixture Models (GMM), Gradient Boosting Machines (GBM), and Multi-Layer Perceptrons (MLP) using... canonical: https://daily.dev/posts/implemented-gmm-gbm-mlp-from-scratch--p8gn5gvsz twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Implemented GMM, GBM & MLP from scratch. | daily.dev og:description: A developer shares from-scratch implementations of Gaussian Mixture Models (GMM), Gradient Boosting Machines (GBM), and Multi-Layer Perceptrons (MLP) using... og:url: https://daily.dev/posts/implemented-gmm-gbm-mlp-from-scratch--p8gn5gvsz og:image: https://api.daily.dev/og/posts/p8gn5GvSZ.png og:image:alt: Implemented GMM, GBM & MLP from scratch. og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Implemented GMM, GBM & MLP from scratch. **[Mahir Malik](https://daily.dev/sources/60gac6pvlv3aiya3cxdek)** · [@mahirmalik](https://daily.dev/mahirmalik) · 1 min read · 0 upvotes · 0 comments ## Summary A developer shares from-scratch implementations of Gaussian Mixture Models (GMM), Gradient Boosting Machines (GBM), and Multi-Layer Perceptrons (MLP) using NumPy, without relying on high-level ML libraries. Key insights include soft clustering advantages of GMM, the iterative error-correction nature of gradient boosting, and the clarity gained from manually implementing backpropagation. A dedicated website and GitHub repo host the implementations, with more paper-based implementations planned. ## Content GMM showed me soft clustering beats hard boundaries for messy data. Gradient boosting is just trees fixing each other's mistakes, and writing backprop in NumPy? pure clarity. Stop calling fit(). Build it. I have hosted a separate website for the implementations, and I will add more paper-based implementations once I'm done with DL basics. Here's the site: [http://papers.mahirmalik.in](http://papers.mahirmalik.in) Also, the GitHub repo: [https://github.com/mahirmlk/papers-implementations.git](https://github.com/mahirmlk/papers-implementations.git) ## Similar posts on daily.dev - [Getting Back to Basics](https://daily.dev/posts/getting-back-to-basics-8dse6eor7) · Simple Thread · 75 upvotes · 2 comments - [Notebook drop - Available only for 48-hours\!](https://daily.dev/posts/notebook-drop---available-only-for-48-hours--efot6xlo9) · Machine Learning Pills · 0 upvotes · 0 comments - [GitHub - angelos-p/llm-from-scratch](https://daily.dev/posts/github---angelos-p-llm-from-scratch-5sw4itutx) · Hacker News · 1 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#neural-networks](https://daily.dev/tags/neural-networks), [#numpy](https://daily.dev/tags/numpy) [View this post on daily.dev](https://daily.dev/posts/implemented-gmm-gbm-mlp-from-scratch--p8gn5gvsz) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"DiscussionForumPosting","mainEntityOfPage":"https://daily.dev/posts/implemented-gmm-gbm-mlp-from-scratch--p8gn5gvsz","headline":"Implemented GMM, GBM & MLP from scratch.","text":"A developer shares from-scratch implementations of Gaussian Mixture Models (GMM), Gradient Boosting Machines (GBM), and Multi-Layer Perceptrons (MLP) using NumPy, without relying on high-level ML libraries. 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