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Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

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Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

#111

Post by j2kun »

HybridCurve wrote: post #84
tnecniv wrote: post #76

Earlier quoted context omitted.

The last part was a big hurdle for me as an early undergrad. I was a fairly strong programmer toward the end of high school, and was trying to think of math as programming. That worked for the fairly algorithmic high school stuff and I got good grades, but it made I was awful at writing proofs. I also went through a phase where I used all the logical notation and rules to manipulate it possible in order to make proof…

This is essentially my problem. I started writing programs at a young age and was introduced (unknowingly) to many more advanced mathematical concepts from that perspective rather than through pure mathematics. What was it that helped break this paradigm for you?

I wrote a book: https://pimbook.org

You might find it useful for your situation. The PDF is pay-what-you-want if you don't feel like paying for it.

Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

#112

Post by fourier456 »

two_in_one wrote: post #110

It's hard to call comprehensive. Transformers - one page. A picture would be nice. No "prompt engineering", no "double deep". In fact words "prompt" and "double" aren't used at all. "Recognition" is used only once outside of bibliography just for reference. Looks like theory will not catch up with practice any time soon. With looming singularity it's bit worrying.

This just isn’t really a good book. One sign; it’s an arXiv book.

Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

#113

Post by [deleted] »

godelski wrote: post #65
joe_the_user wrote: post #35

Earlier quoted context omitted.

If you don't have math you're going to hoodwink yourself into thinking you can get to AGI by scale alone. There are very smart people who think we can get to AGI by scale alone - they call that the "the scaling hypothesis", in fact. I think they're wrong but I thought they knew a fair amount of math. What math would you use to describe the limitations of deep learning? My impression is there aren't any exact theorems…

> "the scaling hypothesis" Oh boy, don't get me started.... I first off should say that by no means do I think any of these people (at least those publishing) are dumb. You can also be a genius in one direction and a fucking idiot in another, and that's okay. Certainly describes me haha (well less on the genius side and more on the functioning idiot side. So take everything I say with a grain of salt). Don't get me w…

[deleted]

Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

#114

Post by nighthawk454 »

currymj wrote: post #107
vivzkestrel wrote: post #96

[flagged]

if you have no mathematical background at all this isn’t the book for you i think. that is not really advanced mathematics although a little notationally dense. there are many good materials such as the fantastic fast.ai course that don’t require such mathematical background. if you are motivated to learn about ML, then studying the topic can gradually be a route in to more mathematical knowledge so that equations li…

Yeah but on the other hand, this is a pretty useless way to give intuition on a concept as simple as mean squared error.

Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

#115

Post by [deleted] »

godelski wrote: post #65
joe_the_user wrote: post #35

Earlier quoted context omitted.

If you don't have math you're going to hoodwink yourself into thinking you can get to AGI by scale alone. There are very smart people who think we can get to AGI by scale alone - they call that the "the scaling hypothesis", in fact. I think they're wrong but I thought they knew a fair amount of math. What math would you use to describe the limitations of deep learning? My impression is there aren't any exact theorems…

> "the scaling hypothesis" Oh boy, don't get me started.... I first off should say that by no means do I think any of these people (at least those publishing) are dumb. You can also be a genius in one direction and a fucking idiot in another, and that's okay. Certainly describes me haha (well less on the genius side and more on the functioning idiot side. So take everything I say with a grain of salt). Don't get me w…

[deleted]

Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

#116

Post by vivzkestrel »

currymj wrote: post #107
vivzkestrel wrote: post #96

[flagged]

if you have no mathematical background at all this isn’t the book for you i think. that is not really advanced mathematics although a little notationally dense. there are many good materials such as the fantastic fast.ai course that don’t require such mathematical background. if you are motivated to learn about ML, then studying the topic can gradually be a route in to more mathematical knowledge so that equations li…

all I am saying is every deep learning book I have ever opened is filled with mathematical stuff like this. I want to learn the mathematics for it but I need a starting point. Isnt there atleast one book in the entire world written with this in mind?

Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

#117

Post by vivzkestrel »

CamperBob2 wrote: post #106
vivzkestrel wrote: post #96

[flagged]

Make the robot overlords work for you. ChatGPT 4's data analysis plugin takes a pretty impressive stab at explaining it: https://i.imgur.com/DCAnoCu.png It will also make an attempt at turning the expressions into Python. It bombed out at first but caught itself and retried without any additional prompting: https://i.imgur.com/nzywKkb.png Not being familiar with SymPy, though, and not having time to think it through…

as a guy not familiar with this math, i am not sure if i should rely on Chat GPT or not. I use GPT only for tasks I am familiar with

Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

#118

Post by HybridCurve »

j2kun wrote: post #111
HybridCurve wrote: post #84

Earlier quoted context omitted.

This is essentially my problem. I started writing programs at a young age and was introduced (unknowingly) to many more advanced mathematical concepts from that perspective rather than through pure mathematics. What was it that helped break this paradigm for you?

I wrote a book: https://pimbook.org You might find it useful for your situation. The PDF is pay-what-you-want if you don't feel like paying for it.

Ah, I think I remember bookmarking this when it was posted before. You really don't have to go very far in computing to find a frontier where most everything in described pure mathematics and so it becomes a substantial barrier for undiversified autodidacts in the field. The math in these areas can often be quite advanced and difficult to approach without the proper background and so I appreciate anyone who has made taken the time to make it less formidable to others.

Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

#119

Post by CamperBob2 »

vivzkestrel wrote: post #117
CamperBob2 wrote: post #106

Earlier quoted context omitted.

Make the robot overlords work for you. ChatGPT 4's data analysis plugin takes a pretty impressive stab at explaining it: https://i.imgur.com/DCAnoCu.png It will also make an attempt at turning the expressions into Python. It bombed out at first but caught itself and retried without any additional prompting: https://i.imgur.com/nzywKkb.png Not being familiar with SymPy, though, and not having time to think it through…

as a guy not familiar with this math, i am not sure if i should rely on Chat GPT or not. I use GPT only for tasks I am familiar with

You definitely shouldn't rely on it.

But you should use it.

Re: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

#120

Post by eightysixfour »

vivzkestrel wrote: post #116
currymj wrote: post #107

Earlier quoted context omitted.

if you have no mathematical background at all this isn’t the book for you i think. that is not really advanced mathematics although a little notationally dense. there are many good materials such as the fantastic fast.ai course that don’t require such mathematical background. if you are motivated to learn about ML, then studying the topic can gradually be a route in to more mathematical knowledge so that equations li…

all I am saying is every deep learning book I have ever opened is filled with mathematical stuff like this. I want to learn the mathematics for it but I need a starting point. Isnt there atleast one book in the entire world written with this in mind?

A Hands-On Introduction to Machine Learning by Chirag Shah is a great starting point IMO.
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