Generative deep learning : teaching machines to paint, write, compose, and play / David Foster
Material type: TextPublication details: India Shroff publishers 2019Description: 308 pISBN:- 9789352138715
- 006.31 FOS-D
Item type | Current library | Collection | Shelving location | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|---|
Books | BITS Pilani Hyderabad | 003-007 | General Stack (For lending) | 006.31 FOS-D (Browse shelf(Opens below)) | Checked out | 22/08/2024 | 40538 |
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavours 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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