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Generative Deep Learning (Paperback)
原價:
HK$700.00
現售:
HK$665
節省:
HK$35 購買此書 10本或以上 9折, 60本或以上 8折
抱歉! 此商品已售罄, 不能訂購
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出版社: |
O'Reilly Media
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出版日期: |
2019/07/16 |
尺寸: |
233x178x28mm |
重量: |
0.52 kg |
ISBN: |
9781492041948 |
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商品簡介 |
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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