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Generative Deep Learning By David Foster PDF

Download Book : Generative Deep Learning By David Foster PDF


Informations about the book:



Title: Generative Deep Learning

Author: David Foster

Size40 MB

FormatPDF

Year2019

Pages566

Book Contents:


chapter 1: Generative Modeling
chapter 2: Deep Learning
chapter 3: Variational Autoencoders
chapter 4: Generative Adversarial Networks
chapter 5: Paint
chapter 6: Write
chapter 7: Compose
chapter 8: Play
chapter 9: The Future of Generative Modeling
chapter 10: Conclusion

Description:


Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.
Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative.
• Discover how variational autoencoders can change facial expressions in photos
• Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation
• Create recurrent generative models for text generation and learn how to improve the models using attention
• Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting
• Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGA



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