Review of David Foster's Generative Deep Learning Book
Explore Insights from Generative Deep Learning Book Reviews

Review of David Foster's Generative Deep Learning Book

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.

Best for
  • Data Scientists
  • Machine Learning Engineers
  • AI Researchers
  • Software Developers
  • Educational Technologists

Starting from $18, you can purchase the complete report, offering advanced filters, buyer personas, and SWOT analysis, with Excel and PDF export options.

Purchased users
53 users purchased a report
in the last 24 hours.
One-time purchase without hidden or recurring costs!
Preview

Here, you can see the limited preview of this report for only 30 reviews.

Download
Custom Date - May 17, 2024
Popular Phrases (of 30 reviews)
see all >>
Featured Content
see all >>
  • 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
  • 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
  • See All Reviews
Sentiment Analysis (of 30 reviews)
73 Net Promoter
Score
Popular Topics (of 30 reviews)
Languages (of 30 reviews)
Send to your inbox!
Send to your inbox!

Have the complete report in your inbox with Excel and PDF attachments. Need to play with the data? Enjoy our lightning-fast dashboard for further analysis.

See a Sample Report Buy Now
One-time purchase without hidden or recurring costs!
"The most valuable form
of customer feedback"

Every purchase starts a new scraping process, so you can have a report containing the analysis of the most recent reviews. It's fast, it's easy, and it brings you the most valuable form of customer feedback.

Purchased users
53 users purchased a report in the last 24 hours.
See a Sample Report Purchase This Report
One-time purchase without hidden or recurring costs!
Similar Reports
  1. Master Modern Computer Vision with Our Expert PyTorch Guide
    Amazon Product Reviews 73
  2. Master TensorFlow: Insights from Our Customer Feedback Report
    Amazon Product Reviews 67
  3. Explore Essential Insights: Machine Learning for Computer Vision
    Amazon Product Reviews 71
Frequently Asked Questions

  • This page displays purchase options and a preview of the customer feedback analysis report for Generative Deep Learning: Teaching Machines... by Foster, David based on online reviews collected from Amazon. The analysis helps Data Scientists, Machine Learning Engineers, AI Researchers, Software Developers, Educational Technologists to discover insights into what people love, dislike, and need.

  • This report provides the Net Promoter Score, sentiment analysis, content classification, most popular phrases, languages and a trend graph to display how the context changes over time. After purchasing the report, you can search in customer feedback and filter results based on classification labels and popular phrases. Also, the analysis is available in PDF format, and all the reviews are available in Excel.

  • Yes, this page is publicly available for everyone on the internet to see. You can copy and share this link with a friend, college or on social media.

  • Yes, you can, and this is the fun part about Kimola! Kimola turns customer feedback into market research by analyzing online reviews from various sources.

  • Yes, you can have this research report analyzed with more reviews. The page you are viewing displays a limited preview with the analysis of 30 recent reviews. Starting from $18, you can buy the complete version of this research report containing all reviews. Click here to see the purchase options.

Legal Disclaimer

We would like to clarify that this product is not created by Amazon and is not associated with or supported by Amazon in any way.

As a user of this web scraping tool, you are solely responsible for complying with all applicable laws and adhering to Amazon's terms of use, including copyright regulations, when utilizing the extracted review data.