Meta Description: "Dive into the world of generative modeling with Amazon's Generative Deep Learning book. Learn to instruct machines in painting, writing, composing music. Turn AI into an artist with practical lessons on variational autoencoders, GANs, and more."
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| generative deep learning book review 2023 |
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.Generative Deep Learning Book
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| generative deep learning book |
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.Generative .Deep Learning Book
- Discover how variational autoencoders can change facial expressions in photos .BUY ON AMAZON CLICK HERE
- 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. NEW OFFERS 2023
- 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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| Generative Deep Learning Book |
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 Generative Deep Learning Book
👉Customer Review .
Reviewed in USA on 4 June 2020Verified Purchase
Awesome book !! The author has very good writing skills. He has explained complex theories by pictorial & diagrammatic way which makes things really cool. Specially GAN, Text generation parts were very good. Some mathematical theories were also well covered .Generative Deep Learning Book
Reviewed in USA on 1 August 2023Author has beautifully crafted engaging stories behind the intuition of various generative models which are typically dry to read. Completed the book in a day like a story book !
-Each block of code snippets are nicely explained.
-Typos are present here and there. But I hope it will be corrected in the next edition.
Hope to see many books like this in the future.Generative Deep Learning Book
Reviewed in India on 15 july 2023 Verified Purchase
before having this book, I was an absolute beginner in generative deep learning. Now, I at least have understood some concepts along with code. not giving 5 stars because this is a black and white print, many concepts mentioned here refer to coloured diagrams, but the diagrams are black and white
Reviewed in the United States on 7 August 2023Verified Purchase
The book starts great. Fantastic examples. It appeals to the reader's intuition and imagination. I loved the beginning and it was very easy working side by side with Jupyter Notebook. The examples are easy to follow and the code is pure Python with Keras. At that point I was going to give the book five stars. However, I was stuck at Autoencoders when the author suddenly started using his own code shortcuts, which was completely unexpected. It took me a while to figure it out that the code was no longer Keras but the functions and objects developed by the author, and imported from the local python files. The book does not explain any of this and the code becomes very obscure. The author's "models" and "utilities" were clearly meant to simplify development of complex neural networks by the reader. Unfortunately, the code is no longer intelligible as it hides the true Keras APIs. These shortcuts are not really necessary and the code they replace would not add much to the size of the book. In those circumstances, if you move away from the book and the author's Github repository, you will no longer be able to reproduce the models and their tests easily. While it is expected of any practitioner to develop his or her own helper library, this is not suitable for the book which needs simplicity and clarity. In all honesty, the book does not claim to train the reader in Keras at all, however, it uses Keras and asks the reader to install the software, and then explains the basics of model creation with Keras, only to leave it behind. I'd recommend to replace all obscure code with the simplest model creation, which can be found in any Keras example on the web. As the author is quite responsive to the reviews and open for comments, I have increased my rating . Generative Deep Learning Book
Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models ..
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