The Engineer’s Guide to Deep Learning: Understanding the Transformer Model

#1

Post by shxx » Tue, Jul 16, 2024, 7:01 AM UTC

The Engineer’s Guide to Deep Learning: Understanding the Transformer Model
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Re: The Engineer’s Guide to Deep Learning: Understanding the Transformer Model

#2

Post by gregw2 » Tue, Jul 16, 2024, 8:02 AM UTC

No content besides a few paragraphs of intro. Actual content has 404 not found errors.

Re: The Engineer’s Guide to Deep Learning: Understanding the Transformer Model

#3

Post by [deleted] » Tue, Jul 16, 2024, 8:05 AM UTC

gregw2 wrote: post #2

No content besides a few paragraphs of intro. Actual content has 404 not found errors.

[deleted]

Re: The Engineer’s Guide to Deep Learning: Understanding the Transformer Model

#4

Post by gpnt » Tue, Jul 16, 2024, 8:05 AM UTC

gregw2 wrote: post #2

No content besides a few paragraphs of intro. Actual content has 404 not found errors.

The menu on the left on desktop is working. It seems only the links on the first page are broken.

Re: The Engineer’s Guide to Deep Learning: Understanding the Transformer Model

#5

Post by smokel » Tue, Jul 16, 2024, 8:06 AM UTC

gregw2 wrote: post #2

No content besides a few paragraphs of intro. Actual content has 404 not found errors.

The links in the running text are broken. The links in the hamburger menu work fine.

Re: The Engineer’s Guide to Deep Learning: Understanding the Transformer Model

#6

Post by _giorgio_ » Tue, Jul 16, 2024, 8:10 AM UTC

It uses keras, which is obsolete. Nobody uses that thing anymore.

Stay away from this.

Re: The Engineer’s Guide to Deep Learning: Understanding the Transformer Model

#7

Post by tuyguntn » Tue, Jul 16, 2024, 8:40 AM UTC

question to experts of HN in ML/AI. Could you please share the beginner resources you think would worth for a person who wants to switch their domain from CRUD/backend APIs to ML/AI. There seems to be many branches of this domain, not sure where to start.

Is my understanding correct?

    * ML engineer -> engineer who builds ML models with pytorch (or similar frameworks)
    * AI engineer -> engineer who builds applications on top of AI solutions (prompt engineering, OpenAI, Claude APIs,....)
    * ML ops -> people who help with deploying, serving models

Re: The Engineer’s Guide to Deep Learning: Understanding the Transformer Model

#8

Post by qsort » Tue, Jul 16, 2024, 8:45 AM UTC

tuyguntn wrote: post #7

question to experts of HN in ML/AI. Could you please share the beginner resources you think would worth for a person who wants to switch their domain from CRUD/backend APIs to ML/AI. There seems to be many branches of this domain, not sure where to start. Is my understanding correct? * ML engineer -> engineer who builds ML models with pytorch (or similar frameworks) * AI engineer -> engineer who builds applications o…

None of these terms have a formal definition. The only association rule you need is:

* Fancy Title -> Whatever the company wants it to be.

All of the above could realistically span from "does bleeding-edge work" to "has once opened a CSV".

Re: The Engineer’s Guide to Deep Learning: Understanding the Transformer Model

#9

Post by shubham13596 » Tue, Jul 16, 2024, 8:48 AM UTC

Very good resource on building from the basics in a concise manner.

Re: The Engineer’s Guide to Deep Learning: Understanding the Transformer Model

#10

Post by MAXPOOL » Tue, Jul 16, 2024, 9:06 AM UTC

There are many others that are better.

1/ The Annotated Transformer Attention is All You Need http://nlp.seas.harvard.edu/annotated-transformer/

2/ Transformers from Scratch https://e2eml.school/transformers.html

3/ Andrej Karpathy has really good series of intros: https://karpathy.ai/zero-to-hero.html Let's build GPT: from scratch, in code, spelled out. https://www.youtube.com/watch?v=kCc8FmEb1nY GPT with Andrej Karpathy: Part 1 https://medium.com/@kdwa2404/gpt-with-andrej-karpathy-part-1...

4/ 3Blue1Brown: But what is a GPT? Visual intro to transformers | Chapter 5, Deep Learning https://www.youtube.com/watch?v=wjZofJX0v4M Attention in transformers, visually explained | Chapter 6, Deep Learning https://www.youtube.com/watch?v=eMlx5fFNoYc Full 3Blue1Brown Neural Networks playlist https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_6700...