4 ms·

What is the equivalent book for Deep Learning?

phonebucket 6y ago
To my mind: https://www.deeplearningbook.org/ https://www.deeplearningbook.org/
blackbear_ 6y ago
Goodfellow's [1] comes closest I'd say, but since deep learning is still moving very fast and lacks a solid formal grounding (unlike most other machine learning methods) it's not so easy to find a comprehensive book yet. [1] https://www.deeplearningbook.org/ https://www.deeplearningbook.org/
- mrfusion 6y ago
  Will that teach me transformers too?
  - nmfisher 6y ago
    No (at least, not the version I read a few years ago). But transformers are a specific neural network architecture, so I’d still recommend the book for the fundamentals around backpropagation, activation & loss functions, etc. Once you’re comfortable with neural networks (and the notation), the “Attention is all you need” paper is fairly accessible.
    - mrfusion 6y ago
      > Once you’re comfortable with neural networks (and the notation) I think I am actually. And I read attention is all you need. (Well half of it) and it didn’t seem to delve into how they work.
      - blackbear_ 6y ago
        It really does, read section 3 carefully. As for why they work so well, that's still an open question.
  - AlexCoventry 6y ago
    For transformers, try this (also links to a video introduction): http://jalammar.github.io/illustrated-transformer/ http://jalammar.github.io/illustrated-transformer/
  - huitzitziltzin 6y ago
    Covered in "Dive Into Deep Learning," which another commenter has recommended. https://d2l.ai/chapter_attention-mechanisms/transformer.html https://d2l.ai/chapter_attention-mechanisms/transformer.html
credit_guy 6y ago
Maybe "Dive into Deep Learning" https://d2l.ai/d2l-en.pdf https://d2l.ai/d2l-en.pdf
huitzitziltzin 6y ago
I really like Aggarwal's "Neural Networks and Deep Learning". I recommend it highly. (At least this summer Springer was giving away a PDF - not sure if that's still true.) To me, Goodfellow et al. spent the first hundred and fifty pages on stuff which is important, but covered better elsewhere (e.g., probability theory, numerical methods) and didn't belong in their book at all. Simultaneously, I didn't get that much out of the "core" chapters on RNNs, CNNs, etc, relative to what I got out of other books. I think the book is somewhat overrated, frankly, but YMMV!
- selimthegrim 6y ago
  Aggarwal has a good explanation of backpropagation

To my mind: https://www.deeplearningbook.org/ https://www.deeplearningbook.org/

Goodfellow's [1] comes closest I'd say, but since deep learning is still moving very fast and lacks a solid formal grounding (unlike most other machine learning methods) it's not so easy to find a comprehensive book yet. [1] https://www.deeplearningbook.org/ https://www.deeplearningbook.org/

mrfusion 6y ago
Will that teach me transformers too?
- nmfisher 6y ago
  No (at least, not the version I read a few years ago). But transformers are a specific neural network architecture, so I’d still recommend the book for the fundamentals around backpropagation, activation & loss functions, etc. Once you’re comfortable with neural networks (and the notation), the “Attention is all you need” paper is fairly accessible.
  - mrfusion 6y ago
    > Once you’re comfortable with neural networks (and the notation) I think I am actually. And I read attention is all you need. (Well half of it) and it didn’t seem to delve into how they work.
    - blackbear_ 6y ago
      It really does, read section 3 carefully. As for why they work so well, that's still an open question.
- AlexCoventry 6y ago
  For transformers, try this (also links to a video introduction): http://jalammar.github.io/illustrated-transformer/ http://jalammar.github.io/illustrated-transformer/
- huitzitziltzin 6y ago
  Covered in "Dive Into Deep Learning," which another commenter has recommended. https://d2l.ai/chapter_attention-mechanisms/transformer.html https://d2l.ai/chapter_attention-mechanisms/transformer.html

credit_guy 6y ago

Maybe "Dive into Deep Learning" https://d2l.ai/d2l-en.pdf https://d2l.ai/d2l-en.pdf

huitzitziltzin 6y ago

I really like Aggarwal's "Neural Networks and Deep Learning". I recommend it highly. (At least this summer Springer was giving away a PDF - not sure if that's still true.) To me, Goodfellow et al. spent the first hundred and fifty pages on stuff which is important, but covered better elsewhere (e.g., probability theory, numerical methods) and didn't belong in their book at all. Simultaneously, I didn't get that much out of the "core" chapters on RNNs, CNNs, etc, relative to what I got out of other books. I think the book is somewhat overrated, frankly, but YMMV!

selimthegrim 6y ago
Aggarwal has a good explanation of backpropagation