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Generative AI with Python and TensorFlow 2

You're reading from Generative AI with Python and TensorFlow 2 Create images, text, and music with VAEs, GANs, LSTMs, Transformer models

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800200883

Length 488 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Authors (2):

Raghav Bali

Joseph Babcock

View More author details

Table of Contents (16) Chapters

Preface

1. An Introduction to Generative AI: "Drawing" Data from Models

2. Setting Up a TensorFlow Lab FREE CHAPTER

3. Building Blocks of Deep Neural Networks

4. Teaching Networks to Generate Digits

5. Painting Pictures with Neural Networks Using VAEs

6. Image Generation with GANs

7. Style Transfer with GANs

8. Deepfakes with GANs

9. The Rise of Methods for Text Generation

10. NLP 2.0: Using Transformers to Generate Text

11. Composing Music with Generative Models

12. Play Video Games with Generative AI: GAIL

13. Emerging Applications in Generative AI

14. Other Books You May Enjoy

15. Index

Paired style transfer using pix2pix GAN

In Chapter 6, Image Generation with GANs, we discussed a number of innovations related to GAN architectures that led to improved results and better control of the output class. One of those innovations was conditional GANs. This simple yet powerful addition to the GAN setup enabled us to navigate the latent vector space and control the generator to generate specific outputs. We experimented with a simple MNIST conditional GAN where we were able to generate the output of our choice.

In this section, we will cover a variant of conditional GANs in the context of style transfer. We will go through details of the pix2pix architecture, discuss the important components and also train a paired style transfer network of our own. We will close this section with some amazing and innovative use cases of such a capability.

Style transfer is an intriguing research area, pushing the boundaries of creativity and deep learning together. In their work...

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Authors (2)

Joseph Babcock

Joseph Babcock

Joseph Babcock has spent over a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Throughout his career, he has worked on recommender systems, petabyte-scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to drug discovery and genomics.

See other products by Joseph Babcock

Raghav Bali

Raghav Bali

Raghav Bali is a Staff Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 12+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications. Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored multiple well received books. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.

See other products by Raghav Bali

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