Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models

by Joseph Babcock, Raghav Bali

Overview

Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI?

In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks.

There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment.

Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation.

Product Details

ISBN-13:	9781800208506
Publisher:	Packt Publishing
Publication date:	04/30/2021
Sold by:	Barnes & Noble
Format:	eBook
Pages:	488
File size:	30 MB
Note:	This product may take a few minutes to download.

About the Author

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.
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.

Table of Contents

Table of Contents

1. An Introduction to Generative AI: "Drawing" Data from Models
2. Setting Up a TensorFlow Lab
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