Learning Generative Adversarial Networks: Next-generation deep learning simplified by Kuntal Ganguly (PDF)

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    Ebook Info

    • Published:
    • Number of pages:
    • Format: PDF
    • File Size: 10.73 MB
    • Authors: Kuntal Ganguly

    Description

    Very good condition, fast delivery

    User’s Reviews

    Reviews from Amazon users which were colected at the time this book was published on the website:

    ⭐Read the papers instead

    ⭐This is an excellent book and probably the first book on Generative Adversarial Network GANs . I found this book to provide a good conceptual overview of the Generative Adversarial Networks GANs and its variant architectures (SRGAN, CGAN, DCGAN, BEGAN, DiscoGAN, StackGAN Deep Dreaming and VAE) through real-world example with public datasets like (fashion MNIST, LFW, CelebA, 101 Object, Kaggle Cat vs Dog, Stanford Cars, and many more). And it’s has a well-written introduction to the basic ideas of deep learning and how it can be used for creativity or in unsupervised domain.I’m not an AI expert, but this book gave me the hands-on real-world example with proper explanation to understand how to build a generative deep neural network and deploy in production.I got this book as I was excited about GANs and was shocked at the clarity of the conceptual explanations and proper code implementations. I have just read the 2nd chapter so far.Besides GANs, this books also discuss the very important concepts of Transfer Learning (pre-trained models) to handle smaller dataset in a Deep neural network. And It also shows how to train and run deep models over large distributed cluster using Apache Spark or BigDL.

    ⭐As other reviews mention, the content is on the level of someone just reading the blogs of other people online and putting it all together. Most claims lack any math or solid science to them.Might be useful as a source of references to recent research though, where you would more sound explanations.The writer did not check the sample code. And it just collect repositories from Github. GANs are not coded in same library, so I think the writer is even not familiar with GAN.

    Keywords

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    Download Learning Generative Adversarial Networks: Next-generation deep learning simplified PDF Free
    Learning Generative Adversarial Networks: Next-generation deep learning simplified PDF Free Download
    Download Learning Generative Adversarial Networks: Next-generation deep learning simplified PDF
    Free Download Ebook Learning Generative Adversarial Networks: Next-generation deep learning simplified

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