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
Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or columns of a 2d feature map, as a standalone package for Pytorch

Triangle Multiplicative Module - Pytorch Implementation of the Triangle Multiplicative module, used in Alphafold2 as an efficient way to mix rows or c

Phil Wang 22 Oct 28, 2022
Pytorch implementation of YOLOX、PPYOLO、PPYOLOv2、FCOS an so on.

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248 Jan 02, 2023
Split your patch similarly to `git add -p` but supporting multiple buckets

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102 Oct 06, 2022
The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".

Energy-based Conditional Generative Adversarial Network (ECGAN) This is the code for the NeurIPS 2021 paper "A Unified View of cGANs with and without

sianchen 22 May 28, 2022
The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering"

The source code of the ICCV2021 paper "PIRenderer: Controllable Portrait Image Generation via Semantic Neural Rendering"

Ren Yurui 261 Jan 09, 2023
Code for "The Box Size Confidence Bias Harms Your Object Detector"

The Box Size Confidence Bias Harms Your Object Detector - Code Disclaimer: This repository is for research purposes only. It is designed to maintain r

Johannes G. 24 Dec 07, 2022
Code for Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works

GDAP Code for Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works Environment Python (verified: v3.8) CUDA

45 Oct 29, 2022
Active and Sample-Efficient Model Evaluation

Active Testing: Sample-Efficient Model Evaluation Hi, good to see you here! 👋 This is code for "Active Testing: Sample-Efficient Model Evaluation". P

Jannik Kossen 19 Oct 30, 2022
This is the code for our KILT leaderboard submission to the T-REx and zsRE tasks. It includes code for training a DPR model then continuing training with RAG.

KGI (Knowledge Graph Induction) for slot filling This is the code for our KILT leaderboard submission to the T-REx and zsRE tasks. It includes code fo

International Business Machines 72 Jan 06, 2023
Face recognition. Redefined.

FaceFinder Use a powerful CNN to identify faces in images! TABLE OF CONTENTS About The Project Built With Getting Started Prerequisites Installation U

BleepLogger 20 Jun 16, 2021
A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling"

SelfGNN A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which will appear in Th

Zekarias Tilahun 24 Jun 21, 2022
Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemetic Analysis

TDY-CNN for Text-Independent Speaker Verification Official implementation of Temporal Dynamic Convolutional Neural Network for Text-Independent Speake

Seong-Hu Kim 16 Oct 17, 2022
Addon and nodes for working with structural biology and molecular data in Blender.

Molecular Nodes 🧬 🔬 💻 Buy Me a Coffee to Keep Development Going! Join a Community of Blender SciVis People! What is Molecular Nodes? Molecular Node

Brady Johnston 456 Jan 08, 2023
Equivariant Imaging: Learning Beyond the Range Space

Equivariant Imaging: Learning Beyond the Range Space Equivariant Imaging: Learning Beyond the Range Space Dongdong Chen, Julián Tachella, Mike E. Davi

Dongdong Chen 46 Jan 01, 2023
Second Order Optimization and Curvature Estimation with K-FAC in JAX.

KFAC-JAX - Second Order Optimization with Approximate Curvature in JAX Installation | Quickstart | Documentation | Examples | Citing KFAC-JAX KFAC-JAX

DeepMind 90 Dec 22, 2022
Deep Markov Factor Analysis (NeurIPS2021)

Deep Markov Factor Analysis (DMFA) Codes and experiments for deep Markov factor analysis (DMFA) model accepted for publication at NeurIPS2021: A. Farn

Sarah Ostadabbas 2 Dec 16, 2022
This demo showcase the use of onnxruntime-rs with a GPU on CUDA 11 to run Bert in a data pipeline with Rust.

Demo BERT ONNX pipeline written in rust This demo showcase the use of onnxruntime-rs with a GPU on CUDA 11 to run Bert in a data pipeline with Rust. R

Xavier Tao 14 Dec 17, 2022
On the Analysis of French Phonetic Idiosyncrasies for Accent Recognition

On the Analysis of French Phonetic Idiosyncrasies for Accent Recognition With the spirit of reproducible research, this repository contains codes requ

0 Feb 24, 2022
Self-Adaptable Point Processes with Nonparametric Time Decays

NPPDecay This is our implementation for the paper Self-Adaptable Point Processes with Nonparametric Time Decays, by Zhimeng Pan, Zheng Wang, Jeff M. P

zpan 2 Sep 24, 2022
A Python package to create, run, and post-process MODFLOW-based models.

Version 3.3.5 — release candidate Introduction FloPy includes support for MODFLOW 6, MODFLOW-2005, MODFLOW-NWT, MODFLOW-USG, and MODFLOW-2000. Other s

388 Nov 29, 2022