Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models
About this ebook
- 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
- 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
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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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.
