Deep Learning Visuals contains 215 unique images divided in 23 categories

Overview

Deep Learning Visuals

Shield: CC BY 4.0

This repository was inspired by the ML Visuals repository maintained by dair.ai.

Deep Learning Visuals contains 215 unique images divided in 23 categories (some images may appear in more than one category). All the images were originally published in my book "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide".

Can I Freely Use These Images?

Sure, these images can be FREELY USED in your own blog posts, slides, presentations, or papers under the CC-BY license.

Awesome, where are they?

You can easily navigate through the pages and indices, and click on the desired image to visualize it in full size:

How Can I Use Them?

DISCLAIMER: this is NOT legal advice, you should always read the license yourself!

In a nutshell, you're allowed to use (or adapt) these images in your own materials, even for commercial purposes, as long as you attribute it.

Here is a quick guide on Best Practices for Attribution.

Here are some examples of both images and attributions:

Logistic Regression

Image by dvgodoy / CC BY

RNN

Image by dvgodoy / CC BY

Transformer

Image by dvgodoy / CC BY

This work is licensed under a Creative Commons Attribution 4.0 International License.

CC BY 4.0

Owner
Daniel Voigt Godoy
Data scientist, developer, teacher and writer. Author of "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide".
Daniel Voigt Godoy
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Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

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"SOLQ: Segmenting Objects by Learning Queries", SOLQ is an end-to-end instance segmentation framework with Transformer.

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A Broader Picture of Random-walk Based Graph Embedding

Random-walk Embedding Framework This repository is a reference implementation of the random-walk embedding framework as described in the paper: A Broa

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Implementation of the HMAX model of vision in PyTorch

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Here is the diagnostic tool for BMVC 2021 paper Diagnosing Errors in Video Relation Detectors.

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How to Learn a Domain Adaptive Event Simulator? ACM MM, 2021

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