O'Reilly Media
Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
Key Metrics
- David Foster
- O'Reilly Media
- Paperback
- 9781492041948
- 9.1 X 7 X 0.9 inches
- 1.15 pounds
- Computers > Artificial Intelligence - Computer Vision & Pattern Recognition
- English
Secure TransactionBook 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 StyleGAN
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