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Review of David Foster's Generative Deep Learning Book

Review of David Foster's Generative Deep Learning Book

30 reviews between Jul 24, 2019 and May 31, 2022.

A comprehensive review of David Foster's Generative Deep Learning book, covering its strengths, weaknesses, and practicality for readers with different levels of ML knowledge.

Generative Deep Learning: Teaching Machines... by Foster, David
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30 reviews between Jul 24, 2019 and May 31, 2022.

Jul 24, 2019 - May 31, 2022

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  • Great intro to generative deep learning

    Cover basic but important technique of generative model and deep learning such as VAE and different type of GAN in understandable manner.
    Great explanation of key deep learning tools such as convolution and different kinds of loss function.

    Would appreciate if the codes comes with pytorch version (although I did find one repo on GitHub that cover this book chapter in pytorch)

    Looking forward to an updated version that talks about newer technique such as diffusion model

    Sentiment
    • Positive
    Content
    • Generative Models
    May 31, 2022
  • Fantastic book

    This book is written in a way that is perfect for explaining the difficult concepts. Each chapter covers a new concept, and it begins by explaining it with an analogy. Then it explains it in real terms. Then it explains it with explicit math and example code. Lots of high quality colored graphs and examples are scattered throughout the book.

    This is honestly one of the best written textbooks I have ever seen.

    Sentiment
    • Positive
    Content
    • Explanation and Clarity
    May 21, 2022
  • One of my best books. In detail code and explanations for how it works. Recommend.

    This book actually shows code for how filters work. It also explains step by step what each function does. If you are into deep learning this will quickly become one of your best learning tools. It shows code for batch selections and what batches do and how the program calculates how many batch steps per epoch. It shows detailed views of layers and describes tensor array variations. I highly recommend it.

    Sentiment
    • Positive
    Content
    • Code Examples
    Sep 15, 2021
  • very well written

    I read the first part of the book and very impressed by its explanation. However, keep in mind that the reader should be familiar with the basics of neural network

    Sentiment
    • Positive
    Content
    • Basics of Neural Network
    Apr 19, 2021
  • Great book

    This book provides a great motivation behind studying generative models. I have found the explanations to be clear and helpful even on topics I am already familiar with.

    Sentiment
    • Positive
    Content
    • Explanation and Clarity
    Oct 03, 2019
  • This book is the only reason I was able to get my dream job as a Machine Learning Engineer

    David Foster's Generative Deep Learning book was the only reason I was able to get my dream job as a Machine Learning Engineer (even though I have no college education...I cannot express my gratitude 🙏)

    Sentiment
    • Positive
    Content
    • Practical Exercises
    Sep 25, 2019
  • Really good overview of topics for GAN development

    This book is a really fun read with great examples in what;s going on with not only GANs, but also their relation to their variational autoencoder cousins. The examples are fantastic and the book is written incredibly well.

    Sentiment
    • Positive
    Content
    • Code Examples
    Sep 18, 2019
  • Very interesting AI book

    I got the book from a friend, and it was a very interesting read, but unfortunately I don't have enough ML knowledge to understand everything. The author starts from the beginning (GANs)/VAEs, and ends with pretty complex architectures from leading researchers (attention/reinforcement learning/etc). The reader should have a good basic to intermediate understanding/experience with Keras/Tensorflow/GANs to understand everything.

    Sentiment
    • Positive
    Content
    • Challenges and Difficulties
    Sep 01, 2019
  • Pretty bad printing quality.

    Paper is super thin and images seem to have printed by a cheap printer in economy mode.

    I'm not usually picky about these things but it is very noticeable.

    Content wise is OK, I especially liked the explanation about mode collapse in GANs and justification of WGAN losses and why they work. There are many analogies to explain concepts (VAEs, GANs, etc) and but I find some of them could be better.

    Sentiment
    • Positive
    Content
    • Quality of Book
    Aug 03, 2019
  • Great contents with lucid examples

    I am half way through the book, and I would give 5 stars to this book. I like the examples that David used to describe VAE and GAN. David is a great writer who balances technical depth and lucid examples to illustrate the key concepts. I would strongly encourage David to write technical books in the near future.

    Sentiment
    • Positive
    Content
    • Explanation and Clarity
    Jul 24, 2019
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