The official code repository for examples in the O'Reilly book 'Generative Deep Learning'

Related tags

Deep LearningGDL_code
Overview

Generative Deep Learning

Teaching Machines to paint, write, compose and play

The official code repository for examples in the O'Reilly book 'Generative Deep Learning'

https://learning.oreilly.com/library/view/generative-deep-learning/9781492041931/

https://www.amazon.com/Generative-Deep-Learning-Teaching-Machines/dp/1492041947/ref=sr_1_1

Tensorflow

This branch uses standalone Keras with a Tensorflow 1.14 backend. See the tensorflow_2 branch for the Keras within Tensorflow 2.0 version of the codebase.

Structure

This repository is structured as follows:

The notebooks for each chapter are in the root of the repository, prefixed with the chapter number.

The data folder is where to download relevant data sources (chapter 3 onwards) The run folder stores output from the generative models (chapter 3 onwards) The utils folder stores useful functions that are sourced by the main notebooks

Book Contents

Part 1: Introduction to Generative Deep Learning

  • Chapter 1: Generative Modeling
  • Chapter 2: Deep Learning
  • Chapter 3: Variational Autoencoders
  • Chapter 4: Generative Adversarial Networks

Part 2: Teaching Machines to Paint, Write, Compose and Play

  • Chapter 5: Paint
  • Chapter 6: Write
  • Chapter 7: Compose
  • Chapter 8: Play
  • Chapter 9: The Future of Generative Modeling
  • Chapter 10: Conclusion

Getting started

To get started, first install the required libraries inside a virtual environment:

pip install -r requirements.txt

Owner
David Foster
Founding Partner, Applied Data Science Partners | Author of 'Generative Deep Learning' (O'Reilly)
David Foster
Python scripts for performing stereo depth estimation using the MobileStereoNet model in Tensorflow Lite.

TFLite-MobileStereoNet Python scripts for performing stereo depth estimation using the MobileStereoNet model in Tensorflow Lite. Stereo depth estimati

Ibai Gorordo 4 Feb 14, 2022
This is an official implementation of the paper "Distance-aware Quantization", accepted to ICCV2021.

PyTorch implementation of DAQ This is an official implementation of the paper "Distance-aware Quantization", accepted to ICCV2021. For more informatio

CV Lab @ Yonsei University 36 Nov 04, 2022
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation

Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation The code of: Cross-Image Region Mining with Region Proto

LiuWeide 16 Nov 26, 2022
Kaggle DSTL Satellite Imagery Feature Detection

Kaggle DSTL Satellite Imagery Feature Detection

Konstantin Lopuhin 206 Oct 29, 2022
Comp445 project - Data Communications & Computer Networks

COMP-445 Data Communications & Computer Networks Change Python version in Conda

Peng Zhao 2 Oct 03, 2022
Mercury: easily convert Python notebook to web app and share with others

Mercury Share your Python notebooks with others Easily convert your Python notebooks into interactive web apps by adding parameters in YAML. Simply ad

MLJAR 2.2k Dec 27, 2022
MARS: Learning Modality-Agnostic Representation for Scalable Cross-media Retrieva

Introduction This is the source code of our TCSVT 2021 paper "MARS: Learning Modality-Agnostic Representation for Scalable Cross-media Retrieval". Ple

7 Aug 24, 2022
End-to-End Speech Processing Toolkit

ESPnet: end-to-end speech processing toolkit system/pytorch ver. 1.3.1 1.4.0 1.5.1 1.6.0 1.7.1 1.8.1 1.9.0 ubuntu20/python3.9/pip ubuntu20/python3.8/p

ESPnet 5.9k Jan 04, 2023
PyTorch IPFS Dataset

PyTorch IPFS Dataset IPFSDataset(Dataset) See the jupyter notepad to see how it works and how it interacts with a standard pytorch DataLoader You need

Jake Kalstad 2 Apr 13, 2022
A Python implementation of active inference for Markov Decision Processes

A Python package for simulating Active Inference agents in Markov Decision Process environments. Please see our companion preprint on arxiv for an ove

235 Dec 21, 2022
Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?

Adversrial Machine Learning Benchmarks This code belongs to the papers: Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness? Det

Adversarial Machine Learning 9 Nov 27, 2022
Code for "MetaMorph: Learning Universal Controllers with Transformers", Gupta et al, ICLR 2022

MetaMorph: Learning Universal Controllers with Transformers This is the code for the paper MetaMorph: Learning Universal Controllers with Transformers

Agrim Gupta 50 Jan 03, 2023
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition

VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition Usage First, install PyTorch 1.7.1+, torchvision 0.8.2

40 Dec 12, 2022
Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks

SSTNet Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks(ICCV2021) by Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui J

83 Nov 29, 2022
Code for the paper "Query Embedding on Hyper-relational Knowledge Graphs"

Query Embedding on Hyper-Relational Knowledge Graphs This repository contains the code used for the experiments in the paper Query Embedding on Hyper-

DimitrisAlivas 19 Jul 26, 2022
Simple codebase for flexible neural net training

neural-modular Simple codebase for flexible neural net training. Allows for seamless exchange of models, dataset, and optimizers. Uses hydra for confi

Jannik Kossen 7 Apr 05, 2022
Paper: De-rendering Stylized Texts

Paper: De-rendering Stylized Texts Wataru Shimoda1, Daichi Haraguchi2, Seiichi Uchida2, Kota Yamaguchi1 1CyberAgent.Inc, 2 Kyushu University Accepted

CyberAgent AI Lab 55 Dec 18, 2022
To provide 100 JAX exercises over different sections structured as a course or tutorials to teach and learn for beginners, intermediates as well as experts

JaxTon 💯 JAX exercises Mission 🚀 To provide 100 JAX exercises over different sections structured as a course or tutorials to teach and learn for beg

Rohan Rao 512 Jan 01, 2023
FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data

FAST Aiming at the problems of cumbersome steps and slow download speed of GNSS data, a relatively complete set of integrated multi-source data download terminal software fast is developed. The softw

ChangChuntao 23 Dec 31, 2022
Implementation of paper "DeepTag: A General Framework for Fiducial Marker Design and Detection"

Implementation of paper DeepTag: A General Framework for Fiducial Marker Design and Detection. Project page: https://herohuyongtao.github.io/research/

Yongtao Hu 46 Dec 12, 2022