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Machine Learning Crash Course

developers.google.com

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Re: Machine Learning Crash Course

#51

Post by cowmoo728 »

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…

We have to separate AI researcher and implementation engineer. These types of crash courses help get you to the point where you can reasonably work under PhD level people and write code to test, scale, and deploy their ideas.

For many current applications of ML this is acceptable because you're just stealing an idea from a paper or stealing ImageNet to recognize your problem. For anything else you really need to pay up and fight with Google for a real expert.

Re: Machine Learning Crash Course

#52

Post by telchar »

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…

This seems relevant: https://open.blogs.nytimes.com/2015/04/09/extracting-structu...

Re: Machine Learning Crash Course

#53

Post by bjourne »

I want to ask people who know ML well if the hype is warranted?

Billions of courses, web sites, job applications and HN posts. The subject seem to have taken off massively in the last two years. I mean image and speech recognition is pretty cool (when it works!), but hardly that earth shattering, is it?

Re: Machine Learning Crash Course

#54

Post by Florin_Andrei »

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…

TLDR: Taking a class is fine, but nothing beats real-world practice.

Wonder where I've heard this one before. :)

Re: Machine Learning Crash Course

#55

Post by telchar »

graycat wrote: post #24

In the course, in lecture "Reducing Loss: Gradient Descent" is "Convex problems have only one minimum; that is, only one place where the slope is exactly 0. That minimum is where the loss function converges." The first sentence is flatly wrong: E.g., for positive integer n and the set of real numbers R, function f: R^n --> R where for all x in R^n f(x) = 0, f is convex, concave, and linear, and for all x in R^n x is…

You should recheck your definitions on convexity.

>function f: R^n --> R where for all x in R^n f(x) = 0

This hyperplane is not convex. A convex curve by definition can not be equal to its tangent at any point.

Edit: I should specify, I mean a convex curve cannot be completely equal to any of its tangents, obviously it will equal each tangent at a single point.

Re: Machine Learning Crash Course

#56

Post by ariwilson »

eanzenberg wrote: post #34
foobiekr wrote: post #25

Earlier quoted context omitted.

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…

Even people getting a quickie masters is hit/miss in my experience. At the end of the day, successful machine learning engineers require a whole suit of different skills, both technical, communicative, and even life skills that don't really exist for software devs. Not all those can be taught in 3 months, 2 years or even 6 years.

Life skills? Communicative skills? What?

Re: Machine Learning Crash Course

#57

Post by ardit33 »

eanzenberg wrote: post #33
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…

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?

Or you can just say "false negative", and every CS major will understand you.

I find people in Math and CS have often very different names for the same type of concepts and they could easy understand each other if they stuck to the more common terms.

In this case, saying: TYPE 2 ERROR, makes you look like you are trying too hard.

Re: Machine Learning Crash Course

#58

Post by disgruntledphd2 »

redditmigrant wrote: post #46

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 mass…

If you are willing to do the work, Frank Harrell's Regression Modeling Strategies is a pretty good introduction to a lot of this.

It's written for a very different set of problems than typical ML, but it has lots of really good advice for practical problems in data analysis and prediction (which is another term for ML).

Mostly people learn this stuff by experience. Find a dataset, choose a predictor, filter, clean and massage your data till you get better metrics/understanding (preferably both). Rinse, repeat on many different datasets and problems, and you'll know how to do this.

Re: Machine Learning Crash Course

#59

Post by achompas »

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…

> 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 the industry.

It is absolutely true that you do not need a graduate degree to apply AI/ML to vanilla problems.

It is also absolutely true, in my experience, that you need a graduate-level education or years of hands-on experience to troubleshoot cases where AI/ML fails on a deceptively-simple problem, or to tweak an AI/ML algorithm (or develop a new one) so it can solve a novel problem.

That said, I think these MOOCs are good enough to get someone to a place where they can create nice /r/dataisbeautiful-style visualizations, or pair with a senior-level DS to deliver something.

(Edited to add folks who have worked on problems for years and add a final note.)

Re: Machine Learning Crash Course

#60

Post by blackrock »

3pt14159 wrote: post #23
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…

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 a "type two" error?

I had to google it. It's a false negative.

A "Type 1" error, is a false positive.

Is this like how people overuse the term "orthogonal"?

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