I've been slowly working my way through this book for the past couple months. It's been amazingly helpful in learning all of the deep learning terminology, and giving a good overview of the technology.
I was doing all of the examples and exercises for a while, but gave up on that at some point. My main goal, after all, was to learn about how the technology works in order to separate the wheat from the chaff, not become an AI researcher.
Ah, thanks for the correction. I saw the title and remembered the earlier discussion - only paid attention to the github repo and not that the link led to a different pdf. I also missed 'review' mentioned here in the title.
>Less common words with strong excess usage included delves (=28.0), showcasing (=10.2) and underscores (=10.9), together with their grammatical inflections (Figure 2a).
But of course, that isn't a smoking gun, it might just be coincidence. The linked arxiv paper itself uses 'delves', probably tongue-in-cheek :)
Okay book, starts well but increasingly relies on math rather than an intuitive description of concepts at least to give you some context, had to go outside of the book at lot on later chapters, and found the diagrams decreasingly useful. The later chapters are more advanced to be fair
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[ 3.2 ms ] story [ 50.0 ms ] threadI was doing all of the examples and exercises for a while, but gave up on that at some point. My main goal, after all, was to learn about how the technology works in order to separate the wheat from the chaff, not become an AI researcher.
This book review is interesting in that it gives a number of the best known books to learn about deep learning.
Some evidence here: https://arxiv.org/html/2406.07016v2
>Less common words with strong excess usage included delves (=28.0), showcasing (=10.2) and underscores (=10.9), together with their grammatical inflections (Figure 2a).
But of course, that isn't a smoking gun, it might just be coincidence. The linked arxiv paper itself uses 'delves', probably tongue-in-cheek :)
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