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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 M
by ldjkfkdsjnv 3y ago
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.
- drBonkers 3y agoWhat would you recommend someone read instead?
- ldjkfkdsjnv 3y agoBetter to understand the bounds of whats currently possible. And then recognize when that changes. Much more economically valuable
- probablynish 3y agoDo you think there's a better way to do this than spending some time playing around with the latest releases of different tools?
- trace_id 3y agoEven better: change the bounds of whats possible ;)
- mindcrime 3y ago> Even better: change the bounds of whats possible ;) ... which will be easier it you have a solid grasp of the foundations of the field. If you only ever focus on the "latest shiny" you'll be lost and left floundering when the landscape changes out from underneath you.
- reqo 3y agoVery few things stay the same in Technology. You should think of technology as another type of evolution! It is driven by the same type of forces as evolution IMO. I think even Linus Torvalds once stated that Linux evolved trough natural selection.
- nabla9 3y ago>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.
- xcv123 3y agoAgreed. 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.
- ldjkfkdsjnv 3y agoNobody 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
- xcv123 3y agoSo 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.
- nabla9 3y ago>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".
- ldjkfkdsjnv 3y agoI know them and am a founder of a vc funded ai startup. Nobody is deploying naive bayes algorithms
- HighFreqAsuka 3y agoQuite 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.
- ldjkfkdsjnv 3y agoSure, 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.
- HighFreqAsuka 3y agoTransformers 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.
- deleted 3y ago[deleted]
- opportune 3y agoThere are plenty of transferrable skills you get from being an expert something that gets made obsolete by a similar-but-different iterative improvement. Maybe you're really good at implementing ideas from papers, you have a great intuitive understanding of how to structure a model to utilize some tech within a particular domain, you understand very well how to implement/use models that require state, you know how to clean and structure data to leverage a particular feature, etc. Also, being an "expert in LSTM" is like being an "expert in HTTP/1.1" or "knowing a lot about Java 8". It's not knowledge or a skill that stands on its own. An expert in HTTP/1.1 is probably also very knowledge about web serving or networking or backend development. HTTP/2 being invented doesn't obsolete the knowledge at all. And that knowledge of HTTP/1.1 would certainly come in handy if you were trying to research or design something like a new protocol, just as knowledge of LSTMs could provide a lot of value for those looking for the next breakthrough in stateful models.
- tysam_and 3y agoHighly, highly disagree. If it became obsolete, then y'all were doing the new shiny. The fundamentals don't really change. There are several different streams in the field, and there are many, many algorithms with good staying power in use. Of course, you can upgrade some if you like, but chase the white rabbit forever, and all you'll get is a handful of fluff.
- wiz21c 3y agoSo, what fundamental stuff should I learn ? I understand ML has some general principles that keeps on bein valid throughout the years. No ?