Generative Deep Learning

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1. rramadassJan 2025
   Some that i am looking into; these are "practical" books which do not focus on the theory/algorithms but given that they are available (library/models/whatever), how to build your apps using them;

   1) Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen.

   2) AI Engineering: Building Applications with Foundation Models by Chip Huyen (this is a very recent book).

   3) Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play by David Foster.

   4) Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG by Bouchard & Peters.

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2. docmechanicApr 2025
   Pretty sure that we’re talking apples and oranges. Yes to the arbitrary byte sequences used by tokenizers, but that is not the topic of discussion. The question is will the tokenizer come up with words not in the training vocabulary. Word tokenizers don’t, but character tokenizers do.

   Source: Generative Deep Learning by David Foster, 2nd edition, published in 2023. From “Tokenization” on page 134.

   “If you use word tokens: …. willnever be able to predict words outside of the training vocabulary.”

   "If you use character tokens: The model may generate sequences of characters that form words outside the training vocabulary."

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3. docmechanicApr 2025
   Source: Generative Deep Learning by David Foster, 2nd edition, published in 2023. From “Tokenization” on page 134.

   “If you use word tokens: …. willnever be able to predict words outside of the training vocabulary.”

   "If you use character tokens: The model may generate sequences of characters that form words outside the training vocabulary."

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4. mindcrimeMar 2024
   I haven't seen any yet. Closest thing to that that I've seen would be something like one of these:

   https://www.amazon.com/Transformers-Natural-Language-Process...

   https://www.amazon.com/Natural-Language-Processing-Transform...

   https://www.amazon.com/Generative-Deep-Learning-Teaching-Mac...

   I wish I could give you a detailed review of all of those, but sadly while I have all three, I've been too slammed with other stuff to make much progress on any of them. :-(

   And while it's not a book, there's that video series by Andrej Karpathy:

   https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThs...

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5. smburdickJan 2024
   I am at times more of a book learner, but find that attitude is often more helpful for non-CS disciplines that change slower (eg, math/physics).

   A recent negative book example for me is the Quantum Programming book from O'Reilly. I found that it did not discuss quantum circuits in a detail that helped me really understand what I was doing -- though I suppose that is a conceit of quantum computing. Perhaps I will return to it later (I am working through Nielsen/Chuang now, which is very theoretical, but explains things very clearly)

   My policy is that a book is nothing more than a learning tool, which a hobby project can also be (perhaps more effectively due to the experience gained).

   Then again, knowledge is power, and books are great at pointing you in the right direction -- assuming you found the right one for your needs, of course.

   I know some people who won't open a book unless they know they can read the whole thing, which I think is a ludicrous attitude.

   I did just order O'Reilly's Generative Deep Learning book, and am hoping to get something out of that, and if I only retain a handful of snippets to use in my career, that is profitable for me.

   The least I can count on is that it will look nice on my shelf.

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