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

Joseph Babcock · Raghav Bali

2021 api. · Packt Publishing Ltd

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This edition is heavily outdated and we have a new edition with PyTorch examples published!Key Features

• Code examples are in TensorFlow 2, which make it easy for PyTorch users to follow along
• Look inside the most famous deep generative models, from GPT to MuseGAN
• Learn to build and adapt your own models in TensorFlow 2.x
• Explore exciting, cutting-edge use cases for deep generative AI

Book DescriptionMachines 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.What you will learn

• Export the code from GitHub into Google Colab to see how everything works for yourself
• Compose music using LSTM models, simple GANs, and MuseGAN
• Create deepfakes using facial landmarks, autoencoders, and pix2pix GAN
• Learn how attention and transformers have changed NLP
• Build several text generation pipelines based on LSTMs, BERT, and GPT-2
• Implement paired and unpaired style transfer with networks like StyleGAN
• Discover emerging applications of generative AI like folding proteins and creating videos from images

Who this book is for

This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.

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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.