Great list. As a newcomer to this field, I too, started out watching YouTube videos and shorts for "trendy ML concepts" but they get overwhelming. I found courses (I've used YouTube playlists or Udemy) to be more structured, and books are the ultimate source of detailed explanations and they build up chapter wise.
I'd like to mention these books that gave me a good introduction to these new AI/ML topics.
- The Illustrated Transformer (Jay)
- LLM Engineer's Handbook (Paul)
- AI Engineering (Chip)
I'm currently going through blogs for more hands-on projects, and that cover conversational topics and help to keep upto date with new trends. Its good to see that many contents are now focusing on production ready applications and best practices. Looking forward to reading your post on content creators for AI-ML content.
"First things first, press the snooze button on the hype distractions.
Mute everything that’s not practical:
Ignore the “influencers” who buzz your feed with tag lines such as “KILLER FEATURE”, “GAME CHANGING”, “HUGE.”
Ignore the memes
Ignore every fresh model and paper release; you won’t keep up.
Ignore the “use these 10000x tools to master AI” messages.
The brain is not wired up to keep track of constant impulses and frequent information overload."
I was just contemplating this morning writing a Substack post on how I'm dealing with AI, and that paragraph covers one of the points I was going to make.
So maybe I'll just quote you! (With a link to your piece, of course. In fact, I'm going to restack it.)
My favorite BS line from the Ai "influencers" is this one: [Some tool/new LLM] JUST STUNNED THE INDUSTRY!" I don't know how many times I read that line during 2024.
I honestly grew tired of typical BS AI influencer stuff, got bored unfollowing people who promote that. The one thing i dislike the most is how "AI expert" everyone has become, people with 0 experience in the field, selling courses, bootcamps in the order of 1000$+, teaching you how to use all these "NEW TOOLS". Total Dunning-Kruger effect at play there :))
This post is eye opener for me. As you said I was totally distracted by hype. At the end i feel confused and stuck. This article is really helpful for me. Thanks Alex!. You are the person i really needed in my ml journey. I would be very much pleased if I would get get your mentorship Alex. I really needed it. Once again thank you Alex.
The notion "Learning is not supposed to be fun" aligns with me a lot. Although, imo, When you are learning it feels like hell because you are literally breaking some neural pathways and creating new ones. And the body does resist the change (inertia). But once you understand what you are learning, it does seem like the hell you went through, was worth it.
The books list is also good with the score.
A suggestion:
If you have read "AI engineering" by Chip Huyen, then that also should be there in here, I think. This is one book that kept me grounded during the AI hype. As your blog talks about cutting through the hype, I think it should be added.
Yes, thank you for your suggestion, completely with you on that!
Chip's book is on my list; I didn't add it here because it's geared towards building with Foundational Models, and in that regard, I'll write another article with a rich set of resources that focuses specifically on that subfield.
I have read half of it(AI engineering). Not yet completed it. Is there something about foundational models too? I was of this notion that it only covered the best practices to build AI app,s what are the questions you should ask before building AI apps and some simple example AI apps to prove the point. Just like her previous book (Desiginig ML sys book) which was less code-intensive and more best practices intense approach .
The thing is, I read the initial few chaps of a book in one sitting only ie 5-6 hours with frequent 30 -40 mins break in between. This tells me that the books speaks to me and i do like to know more what is written. As i forget some of the things, I read in previous sittings, I re-read or skim through what already read. I think I have to re visit it again.
Yes, Chip's AI Engineering book covers foundational models and how to build applications on top of FMs.
Since FMs are inherently different than traditional deep learning models, practices in building AI apps also differ as you now have security, human-in-the-loop, vector databases, LLMOps, and other components.
I would love it if you could share, in an article, your insights on the learning schedule and the AI Engineering book, I think other people would be interested in that too, what do you think?
Great post! One of my toughest moments in the beginning of my ML career was choosing the subfield to specialize in. So many possibilities to use ML in (and the applications of ML can get very very niche specific). Once you fall into a specific niche (like log analysis) it can get hard to apply that knowledge in other areas.
Yes! Wished I thought of that idea and put it in the article :)
I agree, same for me, I started with Deep Learning (Computer Vision) ~8y ago, and figured out even though terms sound like they re'apply across sub-fields, there's a ton more to learn and understand on the side.
Curiosity should be driver. Learning and retention is just a side effect. Thats how you make it fun 🙂
100%, I've got nothing to add to that statement!
Love this post man!!!
Thanks, man! There's so much noise today around ML and too few people who educate and point newcomers in the right direction!
Amazing
Happy to hear that, glad it helped!
Great list. As a newcomer to this field, I too, started out watching YouTube videos and shorts for "trendy ML concepts" but they get overwhelming. I found courses (I've used YouTube playlists or Udemy) to be more structured, and books are the ultimate source of detailed explanations and they build up chapter wise.
I'd like to mention these books that gave me a good introduction to these new AI/ML topics.
- The Illustrated Transformer (Jay)
- LLM Engineer's Handbook (Paul)
- AI Engineering (Chip)
I'm currently going through blogs for more hands-on projects, and that cover conversational topics and help to keep upto date with new trends. Its good to see that many contents are now focusing on production ready applications and best practices. Looking forward to reading your post on content creators for AI-ML content.
I’m taking IBM course on coursera, what do you think about it?
Nice post! That’s what I am trying to do at machine learning at scale :)!
Thanks, man!
Happy to hear that, I've read your latest article LLM Serving (4), great series btw - pure tech, no hype! 🔥
Happy to hear you like it :)
You cleared the confusion. Awesome post.
Thanks for the feedback! Glad to hear that 🔥
I really liked that Chip Huyen book.
100% me too! that book helped me learn and understand the fundamentals of ML Systems, really great read!
Solid recommendations
Thanks man, glad you liked it! 🔥
Nice stack mate
Thanks man 🔥
"First things first, press the snooze button on the hype distractions.
Mute everything that’s not practical:
Ignore the “influencers” who buzz your feed with tag lines such as “KILLER FEATURE”, “GAME CHANGING”, “HUGE.”
Ignore the memes
Ignore every fresh model and paper release; you won’t keep up.
Ignore the “use these 10000x tools to master AI” messages.
The brain is not wired up to keep track of constant impulses and frequent information overload."
I was just contemplating this morning writing a Substack post on how I'm dealing with AI, and that paragraph covers one of the points I was going to make.
So maybe I'll just quote you! (With a link to your piece, of course. In fact, I'm going to restack it.)
My favorite BS line from the Ai "influencers" is this one: [Some tool/new LLM] JUST STUNNED THE INDUSTRY!" I don't know how many times I read that line during 2024.
Happy to hear we share the same thought!
I honestly grew tired of typical BS AI influencer stuff, got bored unfollowing people who promote that. The one thing i dislike the most is how "AI expert" everyone has become, people with 0 experience in the field, selling courses, bootcamps in the order of 1000$+, teaching you how to use all these "NEW TOOLS". Total Dunning-Kruger effect at play there :))
This post is eye opener for me. As you said I was totally distracted by hype. At the end i feel confused and stuck. This article is really helpful for me. Thanks Alex!. You are the person i really needed in my ml journey. I would be very much pleased if I would get get your mentorship Alex. I really needed it. Once again thank you Alex.
Happy to hear that Sai!
Feel free to message me privately If you'd like a specific topic for me to dive into
The notion "Learning is not supposed to be fun" aligns with me a lot. Although, imo, When you are learning it feels like hell because you are literally breaking some neural pathways and creating new ones. And the body does resist the change (inertia). But once you understand what you are learning, it does seem like the hell you went through, was worth it.
The books list is also good with the score.
A suggestion:
If you have read "AI engineering" by Chip Huyen, then that also should be there in here, I think. This is one book that kept me grounded during the AI hype. As your blog talks about cutting through the hype, I think it should be added.
What a comment, I love it!
Yes, thank you for your suggestion, completely with you on that!
Chip's book is on my list; I didn't add it here because it's geared towards building with Foundational Models, and in that regard, I'll write another article with a rich set of resources that focuses specifically on that subfield.
Thank you for your feedback, Nayak
I have read half of it(AI engineering). Not yet completed it. Is there something about foundational models too? I was of this notion that it only covered the best practices to build AI app,s what are the questions you should ask before building AI apps and some simple example AI apps to prove the point. Just like her previous book (Desiginig ML sys book) which was less code-intensive and more best practices intense approach .
The thing is, I read the initial few chaps of a book in one sitting only ie 5-6 hours with frequent 30 -40 mins break in between. This tells me that the books speaks to me and i do like to know more what is written. As i forget some of the things, I read in previous sittings, I re-read or skim through what already read. I think I have to re visit it again.
Yes, Chip's AI Engineering book covers foundational models and how to build applications on top of FMs.
Since FMs are inherently different than traditional deep learning models, practices in building AI apps also differ as you now have security, human-in-the-loop, vector databases, LLMOps, and other components.
I would love it if you could share, in an article, your insights on the learning schedule and the AI Engineering book, I think other people would be interested in that too, what do you think?
Thanks for the encouragement ! It really means a lot!
I will surely do that. I think I will share the learning schedule first then the notes about the books as a follow-up.
Till then I'm going through the Book Review by YT channel "The AI Engineer"
https://youtu.be/U8tC0l06cFQ?feature=shared
And once done, you will be the first to know. Can we connect on Linkedin? I have sent you a request.
I really align with the points mentioned in the video below:
How to take notes:
https://youtu.be/ATmJb3bH2E0?feature=shared
How to read and absorb what you are reading:
https://youtu.be/uiNB-6SuqVA?feature=shared
Very useful 👍
Thanks, Adrian; I'm glad you've enjoyed it. Don't forget to vote in the poll :)) - I'll appreciate your feedback. 🙏
Great post! One of my toughest moments in the beginning of my ML career was choosing the subfield to specialize in. So many possibilities to use ML in (and the applications of ML can get very very niche specific). Once you fall into a specific niche (like log analysis) it can get hard to apply that knowledge in other areas.
Yes! Wished I thought of that idea and put it in the article :)
I agree, same for me, I started with Deep Learning (Computer Vision) ~8y ago, and figured out even though terms sound like they re'apply across sub-fields, there's a ton more to learn and understand on the side.
Overall, nice article. Your recommendations are all good.
Hey, thanks Samith!
Would love to hear the raw feedback aswell, if you have any. 🤘