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Understanding Deep Learning

udlbook.github.io

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Re: Understanding Deep Learning

#21

Post by xcv123 »

nabla9 wrote: post #14
ldjkfkdsjnv wrote: post #4

I spent a decade working on various machine learning platforms at well known tech companies. Everything I ever worked on became obsolete pretty fast. From the ML algorithm to the compute platform, all of it was very transitory. That coupled with the fact that a few elite companies are responsible for all ML innovation, its oxymoronic to me to even learn a lot of this material.

>machine learning platforms Machine learning platforms become obsolete. Machine learning algorithms and ideas don't. If learning SVN or Naive Bayes did not teach you things that are useful today, you didn't learn anything.

Agreed. Look at the table of contents of this book. Whatever fundamental machine learning concepts you learned with SVM or other obsolete algorithms is still useful and applicable today.

Re: Understanding Deep Learning

#22

Post by ldjkfkdsjnv »

nabla9 wrote: post #14
ldjkfkdsjnv wrote: post #4

I spent a decade working on various machine learning platforms at well known tech companies. Everything I ever worked on became obsolete pretty fast. From the ML algorithm to the compute platform, all of it was very transitory. That coupled with the fact that a few elite companies are responsible for all ML innovation, its oxymoronic to me to even learn a lot of this material.

>machine learning platforms Machine learning platforms become obsolete. Machine learning algorithms and ideas don't. If learning SVN or Naive Bayes did not teach you things that are useful today, you didn't learn anything.

Nobody is building real technology with either of those algorithms. Sure, they are theoretically helpful, but they arent valuable anymore. Spending your precious life learning them is a waste

Re: Understanding Deep Learning

#23

Post by ldjkfkdsjnv »

HighFreqAsuka wrote: post #15
ldjkfkdsjnv wrote: post #4

I spent a decade working on various machine learning platforms at well known tech companies. Everything I ever worked on became obsolete pretty fast. From the ML algorithm to the compute platform, all of it was very transitory. That coupled with the fact that a few elite companies are responsible for all ML innovation, its oxymoronic to me to even learn a lot of this material.

Quite a lot of techniques in deep learning have stood the test of time at this point. Also new techniques are developed either depending on or trying to solved deficiencies in old techniques. For example Transformers were developed to solve vanishing gradients in LSTMs over long sequences and improve GPU utilization since LSTMs were inherently sequential in the time dimension.

Sure, but if you were an expert in LSTM, thats nice, you know the lineage of algorithms. But it probably isnt valuable, companies dont care, and you cant directly use that knowledge. You would never just randomly study LSTMs now.

Re: Understanding Deep Learning

#24

Post by xcv123 »

ldjkfkdsjnv wrote: post #22
nabla9 wrote: post #14

Earlier quoted context omitted.

>machine learning platforms Machine learning platforms become obsolete. Machine learning algorithms and ideas don't. If learning SVN or Naive Bayes did not teach you things that are useful today, you didn't learn anything.

Nobody is building real technology with either of those algorithms. Sure, they are theoretically helpful, but they arent valuable anymore. Spending your precious life learning them is a waste

So what? The same fundamental machine learning concepts are still relevant to deep learning.

It's almost like arguing that everything you learned as a Java developer is completely useless when a new programming language replaces it.

Re: Understanding Deep Learning

#26

Post by HighFreqAsuka »

ldjkfkdsjnv wrote: post #23
HighFreqAsuka wrote: post #15

Earlier quoted context omitted.

Quite a lot of techniques in deep learning have stood the test of time at this point. Also new techniques are developed either depending on or trying to solved deficiencies in old techniques. For example Transformers were developed to solve vanishing gradients in LSTMs over long sequences and improve GPU utilization since LSTMs were inherently sequential in the time dimension.

Sure, but if you were an expert in LSTM, thats nice, you know the lineage of algorithms. But it probably isnt valuable, companies dont care, and you cant directly use that knowledge. You would never just randomly study LSTMs now.

Transformers have disadvantages too, and so LSTMs are still used in industry. But also it's not that hard to learn a couple new things every year.

Re: Understanding Deep Learning

#27

Post by quchen »

mnky9800n wrote: post #19
ksherlock wrote: post #13

Earlier quoted context omitted.

Maybe last week's drama should have been a left-pad moment. For many things you can train your own NN and be just as good without being dependent on internet access, third parties, etc. Knowing how things work should give you insight into using them better.

Which drama of last week are you referring to? The one about the openai guy saying it's all just the data set? Or something else?

Their CEO was fired, hired by Microsoft, took a bunch of people with him, and is now back at the company

Re: Understanding Deep Learning

#28

Post by nabla9 »

ldjkfkdsjnv wrote: post #22
nabla9 wrote: post #14

Earlier quoted context omitted.

>machine learning platforms Machine learning platforms become obsolete. Machine learning algorithms and ideas don't. If learning SVN or Naive Bayes did not teach you things that are useful today, you didn't learn anything.

Nobody is building real technology with either of those algorithms. Sure, they are theoretically helpful, but they arent valuable anymore. Spending your precious life learning them is a waste

>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".

Re: Understanding Deep Learning

#29

Post by ldjkfkdsjnv »

nabla9 wrote: post #28
ldjkfkdsjnv wrote: post #22

Earlier quoted context omitted.

Nobody is building real technology with either of those algorithms. Sure, they are theoretically helpful, but they arent valuable anymore. Spending your precious life learning them is a waste

>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

Re: Understanding Deep Learning

#30

Post by nabla9 »

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

> Nobody is deploying naive bayes algorithms

Exactly my point. You are so into user perspective that you think you are arguing against me.

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