14 reviews between Jun 06, 2023 and Oct 26, 2023.
This review is for the Kindle edition of the book Generative Deep Learning. While the content and examples are good, the electronic version has poor quality with skipped type settings and substituted symbols. The book covers key techniques in generative AI but lacks a section on evaluating the quality of generated images. Overall, it is a helpful resource for understanding generative ML, but the formatting of mathematical formulas is a drawback.Generative Deep Learning eBook : Foster, David
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14 reviews between Jun 06, 2023 and Oct 26, 2023.
Give a very good understanding of the field.
Helps create quickly easy models and gives a good grasp of the math underlying the models
This was a great read to understand how generative AI works, at the right level of detail and very much up to date. The content structure is good to learn the theory starting from the basics and then gradually layering the most complex and recent evolutions. The accompanying TensorFlow workbooks help with practical examples that can be followed.
One negative note: the Kindle version is low quality when it comes to mathematical formulas, impossible to read.
This is a lovely book. It is readable and explains the principles behind algorithms clearly.
The big-picture ideas are good, but without the ability to practice the code included, I can't recommend it.
(I spent over ten hours trying to work through compatibility problems, outdated libraries, and unsupported software. In the end, I wasn't able to run a single example code example from this book. I'm not a noob either. I've managed to install and run inference / training on dozens of open source AI projects, for image gen, LLMs, music, voice synthesis, style transfer, NeRFs, and 3d model gens. This book uses Docker and Jupyter notebooks, which I've never seen any other project use ever. They're not supported on modern systems. Obsolete.)
The material is well presented with a good mix of explanation and little stories that help explain how various architectures work, and followed up with code and more mathematical detail. Coming from a computer science background and knowing a little about neural nets I felt I was able to follow along and grasp the ideas due to the authors efforts at presenting the material in a careful order building up to the more complex architectures.
I had to deduct a point for the poor formatting of the mathematical formulas in this book. I thought it might just be a Kindle thing, but checked the paper copy too and its not really massively better there. Hopefully a newer edition can spend more time on the typesetting.
Although the book covers many key techniques in generative AI, a key question needs to be answered, how do we know if it's generating a good quality image other than by eyeballing it?
There should be a section that talks about the joint use of the discriminative model and generative model, for example, if we were using the generative model to augment the dataset for the downstream discriminative task (image classification), how do we evaluate the generated data has been helpful, some may say just look at the performance difference of downstream task, but I bet there is more insight than that, author need to consider this problem in future edition.
David did a great job with this book. He very clearly explains how generative ML (deep learning, AI, etc) works from first principles. Very helpful if you're interested in GAI from a fundamental level.
This review is for the Kindle edition. I have the 2nd print edition of the book and it looks like the 3rd edition has some good additions but rushed out. In this day and age of spell-checkers, auto-complete, and now generative AI, it is unacceptable that the electronic version has poor quality.
Subscripts and other type settings are skipped in many places making it harder to read equations.
For example J = dz 1 dx 1... or det abcd = ad bc. For some reason many kindle books are plagued with some symbol getting substituted with a square . You have to guess what that symbol was supposed to be. For the price, that shouldn't happen.
The irony is this a book about generative AI which is supposed to simplify or at least help in such things. If you can wait, maybe there will be a revised edition.
Amazing book, for me the best thing about it is that there are many well-sourced and working code and data examples which are explained clearly in the text.
When I first started learning how to train generative models, I tried taking online courses, but even the expensive ones from top providers never went deep enough into either the math or the code to truly equip me to engineer novel solutions with this technology—at best I could imitate cookie-cutter solutions. Of all the technical books I've read on software, not just generative deep learning, this one has the clearest explanations and goes into every nitty-gritty detail.
My first edition copy of Generative Deep Learning has been scribbled in, dog-eared, and beaten up over the course of the last few years. Now that I have my second edition, the first will finally get some much-deserved rest on my bookshelf. Technology has evolved quickly and the second edition covers everything I've come across in the field more recently that hadn't been invented yet when the first edition was released.
I've invested a lot in educating myself on machine learning over the past eight years and Generative Deep Learning is by far the best value. I'd recommend it to people of any skill level, as the author does a great job explaining the beginner concepts, but also dives into more advanced topics and analysis than I've seen elsewhere.
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