Reviews
Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster
ravipotter's review against another edition
3.0
3.5/5
It's a great book, awesome explanations but falls short because of some rushed up chapters. The chapters related to computer vision are very good. I'm a Computer Vision Engineer and I loved reading it. Some metaphors used were really good particularly the ones about the variational autoencoders, but some are cheesy and weren't really helpful, I didn't mind these.
The parts related to NLP and attention mechanisms were not good enough. With all due respect to the author, I felt that I could have explained some things better. The intuition part was not covered in these areas as much as when dealing with images. I'd have loved lesser focus on code and more emphasis on the explanation.
And some parts I was not good enough to understand, the ones where he talks of music generation, despite good familiarity with the underlying technologies. So these parts are definitely not for non-practioners. I'd have to do some more reading to understand this music part.
I felt the final few chapters were very rushed. Particularly when NLP started. I'd have loved seeing a fatter book, which is something I don't say often.
After all is said and done, this is still a wonderful book and very entertaining. A must read.
It's a great book, awesome explanations but falls short because of some rushed up chapters. The chapters related to computer vision are very good. I'm a Computer Vision Engineer and I loved reading it. Some metaphors used were really good particularly the ones about the variational autoencoders, but some are cheesy and weren't really helpful, I didn't mind these.
The parts related to NLP and attention mechanisms were not good enough. With all due respect to the author, I felt that I could have explained some things better. The intuition part was not covered in these areas as much as when dealing with images. I'd have loved lesser focus on code and more emphasis on the explanation.
And some parts I was not good enough to understand, the ones where he talks of music generation, despite good familiarity with the underlying technologies. So these parts are definitely not for non-practioners. I'd have to do some more reading to understand this music part.
I felt the final few chapters were very rushed. Particularly when NLP started. I'd have loved seeing a fatter book, which is something I don't say often.
After all is said and done, this is still a wonderful book and very entertaining. A must read.
ravipotter's review
3.0
3.5/5
It's a great book, awesome explanations but falls short because of some rushed up chapters. The chapters related to computer vision are very good. I'm a Computer Vision Engineer and I loved reading it. Some metaphors used were really good particularly the ones about the variational autoencoders, but some are cheesy and weren't really helpful, I didn't mind these.
The parts related to NLP and attention mechanisms were not good enough. With all due respect to the author, I felt that I could have explained some things better. The intuition part was not covered in these areas as much as when dealing with images. I'd have loved lesser focus on code and more emphasis on the explanation.
And some parts I was not good enough to understand, the ones where he talks of music generation, despite good familiarity with the underlying technologies. So these parts are definitely not for non-practioners. I'd have to do some more reading to understand this music part.
I felt the final few chapters were very rushed. Particularly when NLP started. I'd have loved seeing a fatter book, which is something I don't say often.
After all is said and done, this is still a wonderful book and very entertaining. A must read.
It's a great book, awesome explanations but falls short because of some rushed up chapters. The chapters related to computer vision are very good. I'm a Computer Vision Engineer and I loved reading it. Some metaphors used were really good particularly the ones about the variational autoencoders, but some are cheesy and weren't really helpful, I didn't mind these.
The parts related to NLP and attention mechanisms were not good enough. With all due respect to the author, I felt that I could have explained some things better. The intuition part was not covered in these areas as much as when dealing with images. I'd have loved lesser focus on code and more emphasis on the explanation.
And some parts I was not good enough to understand, the ones where he talks of music generation, despite good familiarity with the underlying technologies. So these parts are definitely not for non-practioners. I'd have to do some more reading to understand this music part.
I felt the final few chapters were very rushed. Particularly when NLP started. I'd have loved seeing a fatter book, which is something I don't say often.
After all is said and done, this is still a wonderful book and very entertaining. A must read.
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