explaingit

dair-ai/ml-visuals

Analysis updated 2026-06-24

17,181Audience · researcherComplexity · 1/5Setup · easy

TLDR

A community-maintained Google Slides deck of 100+ machine learning diagrams. Free to copy and reuse in papers, blog posts, and talks if you credit the original designer.

Mindmap

mindmap
  root((ml-visuals))
    Inputs
      Google Slides link
      Community contributions
    Outputs
      PNG figures
      PDF figures
      Editable slides
    Use Cases
      Illustrate a blog post
      Build conference slides
      Add figures to a paper
    Tech Stack
      Google Slides
      Markdown
Click or tap to explore — scroll the page freely

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

What do people build with it?

USE CASE 1

Drop a transformer architecture diagram into a blog post

USE CASE 2

Pull attention and encoder-decoder visuals for a conference talk

USE CASE 3

Use prebuilt CNN and RNN figures in a research paper

USE CASE 4

Contribute a new figure back to the shared deck with author credit

What is it built with?

Google SlidesMarkdown

How does it compare?

dair-ai/ml-visualsidvel/rime-iceranger/ranger
Stars17,18117,18117,178
LanguageLuaPython
Setup difficultyeasymoderateeasy
Complexity1/52/52/5
Audienceresearchergeneraldeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

Needs a Google account to access and copy the slides.

In plain English

ML Visuals is a free, community-built collection of diagrams and figures for explaining machine learning concepts. It is hosted as a Google Slides presentation and maintained through this GitHub repository. The goal is to give researchers, students, and writers ready-made visuals they can copy, customize, and use in blog posts, academic papers, and presentations, without having to draw neural network diagrams from scratch. The collection covers common ML concepts such as attention mechanisms, transformer architectures, encoder-decoder models, recurrent networks (RNNs, LSTMs, GRUs), and convolutional neural networks (CNNs), with more being added over time by community contributors. The README notes the slides already contain over 100 figures. All figures are free to use, the only ask is to credit the original designer, whose name is stored in the slide notes. Using it is straightforward: open the Google Slides link, browse the figures, and download whichever ones you need via File → Download in whatever format you want (PNG, PDF, etc.). If you want to customize a figure, you can request edit access to the shared document or make your own copy. Contributing is also encouraged, you add a new slide with your figure and include author information in the notes so others can credit you. You would reach for ML Visuals when you are writing a technical blog post, preparing a conference talk, or working on a paper and need a clean, clear visual of a standard ML concept but do not want to build the diagram from scratch in a drawing tool.

Copy-paste prompts

Prompt 1
Find me the best transformer attention diagram in ml-visuals and tell me how to export it as PNG
Prompt 2
List which ML concepts have figures available in ml-visuals so I can plan my blog post
Prompt 3
Walk me through forking the ml-visuals Google Slides and customizing a CNN figure for my paper
Prompt 4
Show me how to contribute a new diagram to ml-visuals including author credit in the slide notes

Frequently asked questions

What is ml-visuals?

A community-maintained Google Slides deck of 100+ machine learning diagrams. Free to copy and reuse in papers, blog posts, and talks if you credit the original designer.

How hard is ml-visuals to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is ml-visuals for?

Mainly researcher.

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