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Understanding Deep Learning

udlbook.github.io

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Re: Understanding Deep Learning

#71

Post by galangalalgol »

Slix wrote: post #66
dafaqueue wrote: post #45

Earlier quoted context omitted.

I started off being really excited to learn, but as time went on I actually lost interest in the field. The whole thing is essentially curve fitting. The ML field is essentially an art more than a science and it's all about tricks and intuitions on different ways of getting that best fit curve. From this angle the whole field got way less interesting. The field has nothing deeper or more insightful to offer beyond th…

Are deep learning and neural networks just curve fitting? I thought those were significantly different.

You could argue all the building blocks are forms of curve fits, but that isn't a terribly useful statement even if true. If you can fit a curve to the desired behavior of any function, or composition of functions (which is a function) then you can solve any problem you can express the desired behavior of. Including the expressing of desired behavior for some other class if problems. Saying it is just curve fitting is like saying something is just math. The entirety of reality is just math.

Re: Understanding Deep Learning

#72

Post by yeukhon »

ldjkfkdsjnv wrote: post #29
nabla9 wrote: post #28

Earlier quoted context omitted.

>Spending your precious life learning them is a waste So you really did not learn them. There is nothing wrong with being user. You don't have to know how compilers work to use compiler. But then you should not say you understand compilers. In the same way, you probably would benefit from a book "Using deep learning", not "Understanding deep learning".

I know them and am a founder of a vc funded ai startup. Nobody is deploying naive bayes algorithms

Yeah. But you didn’t build a plane without knowing physics right?

Nobody deploys a textbook algorithm because everyone knows textbooks algorithms and there are no advantages. So, no, there is real value in learning the fundamentals, dear founder.

Re: Understanding Deep Learning

#73

Post by ocharles »

Slix wrote: post #67

If I start now and start reading up on AI, will I become anything close to an expert? I'm worried that I'm starting a journey that requires a Master's or PhD.

Very hard to answer without knowing what your goal is. Do you want to be a practitioner of DL, or do you want to be a researcher?

Re: Understanding Deep Learning

#74

Post by mavelikara »

ocharles wrote: post #73
Slix wrote: post #67

If I start now and start reading up on AI, will I become anything close to an expert? I'm worried that I'm starting a journey that requires a Master's or PhD.

Very hard to answer without knowing what your goal is. Do you want to be a practitioner of DL, or do you want to be a researcher?

Not the OP, but I’d like to hear you answer and reasoning for the “practitioner of DL” case.

Re: Understanding Deep Learning

#75

Post by Chirono »

Slix wrote: post #67

If I start now and start reading up on AI, will I become anything close to an expert? I'm worried that I'm starting a journey that requires a Master's or PhD.

From reading this book you’d have a very good grasp of the underlying theory, much more than many ML engineers. But you’d be missing out on the practical lessons, all the little tips and intuitions you need to be able to get systems working in practice. I think this just takes time and it’s as much an art as it is a science.

Re: Understanding Deep Learning

#76

Post by gardenhedge »

martingoodson wrote: post #68

Most comments here are in one of two camps: 1) you don't need to know any of this stuff, you can make AI systems without this knowledge, or 2) you need this foundational knowledge to really understand what's going on. Both perspectives are correct. The field is bifurcating into two different skill sets: ML engineer and ML scientist (or researcher). It's great to have both types on a team. The scientists will be too s…

Would you see these as analogous?

The people who create the models and the people that use them.

The people who create the programming languages and the people that use them.

Re: Understanding Deep Learning

#77

Post by 3abiton »

martingoodson wrote: post #68

Most comments here are in one of two camps: 1) you don't need to know any of this stuff, you can make AI systems without this knowledge, or 2) you need this foundational knowledge to really understand what's going on. Both perspectives are correct. The field is bifurcating into two different skill sets: ML engineer and ML scientist (or researcher). It's great to have both types on a team. The scientists will be too s…

This sounds like a sell-pitch for an AI scientist.

Re: Understanding Deep Learning

#79

Post by mi_lk »

martingoodson wrote: post #68

Most comments here are in one of two camps: 1) you don't need to know any of this stuff, you can make AI systems without this knowledge, or 2) you need this foundational knowledge to really understand what's going on. Both perspectives are correct. The field is bifurcating into two different skill sets: ML engineer and ML scientist (or researcher). It's great to have both types on a team. The scientists will be too s…

I guess this message is delivered by an AI scientist, sure.

It's almost self-exploratory that when you hit a roadblock in practice you go back to foundations, and good people should aim to do both. In that case I don't see where ML engineer/scientist bifurcation comes from except for some to feel good about themselves

Re: Understanding Deep Learning

#80

Post by wiz21c »

dafaqueue wrote: post #45
deepsquirrelnet wrote: post #31

Earlier quoted context omitted.

This is about deep learning, of which LLMs are a subset. If you are interested in machine learning, then you should learn deep learning. It is incredibly useful for a lot of reasons. Unlike other areas of ML, the nature of deep learning is such that its parts are interoperable. You could use a transformer with a CNN if you wish. Also, deep learning enables you to do machine learning on any type of data, text, images,…

I started off being really excited to learn, but as time went on I actually lost interest in the field. The whole thing is essentially curve fitting. The ML field is essentially an art more than a science and it's all about tricks and intuitions on different ways of getting that best fit curve. From this angle the whole field got way less interesting. The field has nothing deeper or more insightful to offer beyond th…

Although it fundamentally is curve fitting, I'd venture to say that at some point, having to handle millions of parameters makes the curve fitting problem unrecognizable... A change in quantity is a change is nature if you will.

IOW: to me, fitting a generalized linear model is very different than fitting a convolutional network.

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