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Machine Learning Crash Course

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Re: Machine Learning Crash Course

#41

Post by maffydub »

Abundnce10 wrote: post #32

I have a new project at work: I need to take in a free form text of recipe ingredients (e.g. "1/2 cup diced onions", "two potatoes, cut into 1-inch cubes", etc.) and build a program that identifies the ingredient (e.g. onion, potato), as well as the quantity (e.g. 0.5 cup, 2.0 units). Would machine learning be an applicable approach to solving this? Right now I'm just planning on using an NLP library to parse out the…

It's not very sexy, but I think you might find it easier and more robust just to use an NLP library.

I built something similar (albeit for a relatively limited database of recipes) for a hackathon a couple of weeks back. I didn't even use a proper NLP library, just some simple hand-rolled pattern-matching, and got pretty good results.

Good luck!

Re: Machine Learning Crash Course

#42

Post by maffydub »

minimaxir wrote: post #37
eanzenberg wrote: post #33

Earlier quoted context omitted.

You probably can't communicate effectively. If you are describing "Type two error" of course you will get eyes glossing over. A huge problem with research fields is their terse banal labels. Confusion matrix anyone?

Granted, Type II error and confusion matrices are covered in more basic statistical classes, and are indeed important for hypothesis testing.

I think the point the parent might have been making is that many people (or maybe just me) know "type II error" by the far more self-explanatory name of "false negative".

Re: Machine Learning Crash Course

#43

Post by [deleted] »

maffydub wrote: post #39
3pt14159 wrote: post #23

Earlier quoted context omitted.

Unless you work for a company obviously known for their ML the "expertise" out there right now is brutal. People are building recommendation engines without knowing the very, very, very basics like Jaccard indexes, ROC Curves, or topic drift. I've even had to explain type two error to someone working on one of these before. I agree with your general thrust, and you're right, messy data is often 95% of the problem, bu…

For anyone else who wondered what the Jaccard index is, it's also referred to as Intersection over Union. ...and if you haven't come across that either, see https://en.wikipedia.org/wiki/Jaccard_index for details.

[deleted]

Re: Machine Learning Crash Course

#44

Post by Abundnce10 »

maffydub wrote: post #41
Abundnce10 wrote: post #32

I have a new project at work: I need to take in a free form text of recipe ingredients (e.g. "1/2 cup diced onions", "two potatoes, cut into 1-inch cubes", etc.) and build a program that identifies the ingredient (e.g. onion, potato), as well as the quantity (e.g. 0.5 cup, 2.0 units). Would machine learning be an applicable approach to solving this? Right now I'm just planning on using an NLP library to parse out the…

It's not very sexy, but I think you might find it easier and more robust just to use an NLP library. I built something similar (albeit for a relatively limited database of recipes) for a hackathon a couple of weeks back. I didn't even use a proper NLP library, just some simple hand-rolled pattern-matching, and got pretty good results. Good luck!

I think you're right. Did you happen to open-source your code from the hackathon? I'd love to take a look at your approach if you don't mind.

Re: Machine Learning Crash Course

#45

Post by canyon289 »

foobiekr wrote: post #25
minimaxir wrote: post #12

Looking through the topics covered, the standard AI-course caveats ( https://news.ycombinator.com/item?id=16247629 ) apply. Yes, AI/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessible. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about…

Gate keeping is only obsolete when it ceases to have impact. The reality right now is that ML is extremely hard to enter even for a very knowledgeable and deeply experienced but non-credentialed (by degree) person. It will be interesting to see how the situation evolves but my own observations are that people trying to enter the space might be better off getting a quickie masters if they can afford the time or cost t…

I agree with this comment. My experience has been that people don't really look at your resume unless you have machine learning experience on your resume or one of the stats type majors

Re: Machine Learning Crash Course

#46

Post by redditmigrant »

As someone who is trying to learn ML, all the courses available are hugely helpful. One thing I wish I had easy access to is the process that someone goes through while trying to build a model on a real dataset.

Specifically following questions are the ones I struggle with:

1. How did you figure out what features would be useful?

2. How did you figure out what algorithm(s) are appropriate?

3. how and why did you massage the data in a specific way?

Re: Machine Learning Crash Course

#48

Post by icc97 »

minimaxir wrote: post #12

Looking through the topics covered, the standard AI-course caveats ( https://news.ycombinator.com/item?id=16247629 ) apply. Yes, AI/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessible. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about…

I fully agree with you that after a MOOC you've barely scratched the surface and until you're implementing them yourself then you're not going to jump into a ML job.

However personally I view the rest of the opposite way round. Getting through a course on Deep Learning takes months [0]. Then reading through Keras code once you understand the appropriate NNs is easy.

For example it takes a while of going through Neural Networks to understand ResNets. But if you understand ResNets then looking though Keras code that creates a ResNet [1] is easy.

If I want to build a NN of any sort in Keras I can just Google for it. However there's no simple Googling you can do to teach yourself NN in an easy to follow structured way.

[0]: https://www.deeplearning.ai/

[1]: https://github.com/Hyperparticle/one-pixel-attack-keras/blob...

Re: Machine Learning Crash Course

#49

Post by stochastic_monk »

fwdpropaganda wrote: post #28
3pt14159 wrote: post #23

Earlier quoted context omitted.

Unless you work for a company obviously known for their ML the "expertise" out there right now is brutal. People are building recommendation engines without knowing the very, very, very basics like Jaccard indexes, ROC Curves, or topic drift. I've even had to explain type two error to someone working on one of these before. I agree with your general thrust, and you're right, messy data is often 95% of the problem, bu…

What's topic drift?

It seems to be a specialized term referring to the change in focus of blogs [0] and online communities [1] over time. This strikes me as a very specialized concept, rather than a generally-important term in machine learning as a whole.

Edit: To add my perspective, with years of industry experience and graduate-level machine learning coursework, I have never before encountered this term.

[0]: https://link.springer.com/chapter/10.1007/978-3-319-16354-3_...

[1]: http://catb.org/jargon/html/T/topic-drift.html

Re: Machine Learning Crash Course

#50

Post by maffydub »

Abundnce10 wrote: post #44
maffydub wrote: post #41

Earlier quoted context omitted.

It's not very sexy, but I think you might find it easier and more robust just to use an NLP library. I built something similar (albeit for a relatively limited database of recipes) for a hackathon a couple of weeks back. I didn't even use a proper NLP library, just some simple hand-rolled pattern-matching, and got pretty good results. Good luck!

I think you're right. Did you happen to open-source your code from the hackathon? I'd love to take a look at your approach if you don't mind.

Sorry, I normally would but one of the other team members is considering taking the hack forward and wanted to keep it closed for now. (It's hard to see how much competitive advantage he'd have from 48 hours of very-hacked-together code, but so few hackathon projects get taken forward that I didn't want to discourage him!)

The approach was to tokenize the input and then do basic pattern-matching on it, with separate dictionaries of quantity units (e.g. cup, oz, pound) ingredients, processing words (e.g. "chopped") and throw-away words (e.g. "of"). In fact, possibly the most complicated part was parsing "2.5", "2 and a half" and "2½" all to the same thing.

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