Generative Deep Learning : Teaching Machines to Paint, Write, Compose and Play


Generative Deep Learning : Teaching Machines to Paint, Write, Compose and Play
Format Paperback | 350 pages
Dimensions 178 x 233 x 22.86mm | 521.63g
Publication date 01 Aug 2019
Publisher Finelybook 出版社 O'Reilly Media, Inc, USA
Publication City/Country Sebastopol, United States
Language English
ISBN10 1492041947
ISBN13 9781492041948
Bestsellers rank 20,576
The Book Description robot was collected from Amazon and arranged by Finelybook

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 StyleGAN
Preface
1. Introduction to Generative Deep Learning
1. Generative Modeling
2. Deep Learning
3. Variational Autoencoders
4. Generative Adversarial Networks
ll. Teaching Machines to Paint, Write, Compose, and Play
5. Paint
6. Write
7. Compose
8. Play
9. The Future of Generative Modeling
10. Conclusion
Index

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