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Machine Learning Crash Course

developers.google.com

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Re: Machine Learning Crash Course

#161

Post by graycat »

telchar wrote: post #82
tzs wrote: post #67

Earlier quoted context omitted.

It sounds like you are describing curves that are strictly convex. Curves that are convex, but not strictly convex, can intersect their tangents at more than one point, or even at every point. I'm going by the definition of convex function given in Rudin's "Principles of Mathematical Analysis", Apostol's "Calculus", Wikipedia, and MathWorld.

Fair enough. I suppose pointing out that the authors merely omitted "strictly" wouldn't have served GP's point as well.

Strictly convex need have no role in this Google ML material. Just convex is enough.

Re: Machine Learning Crash Course

#162

Post by kahnjw »

achompas wrote: post #106
lugg wrote: post #100

Earlier quoted context omitted.

What's an example of a problem that needs that troubleshooting? (Curious)

Honestly? The exact problem I'm dealing with at work right now. We're trying to re-write our recommender for artist music stations at iHeartRadio (aka "I'll listen to Drake or Kendrick Lamar's station at the gym today"). Just today, I tried adding negative sampling to the matrix I'm factorizing, hoping it encourages spread in the embeddings learned for artists in certain types of genres. I have a MS, but not a lot of…

Aren't you sort of glossing over the fact that he is in high up machine learning position at a company that specializes in recommender systems? Doesn't that by itself increase the likelihood that he deeply understands implicit and explicit matrix factorization?

I am a good ways through my masters (second CS degree, first specializing in ML), and the more I learn, the more I realize that on any given topic, there is no guarantee the PhD in the room has the most expertise. Machine learning is a broad field that contains many subfields, methodologies, and many applications. It is a bit like computer systems or software engineering: nobody knows it all, people who are experts have intimate knowledge of a specific subset of the field. Of course, you can more around over time, but it takes years to build up expertise in even two or three subfields of machine learning.

Side note: sounds like we do similar work. I work at Vevo, also do a lot of matrix factorization to learn latent factors of items such as artists, videos, etc.

Re: Machine Learning Crash Course

#163

Post by achompas »

Retric wrote: post #153
achompas wrote: post #137

Earlier quoted context omitted.

Yeah, I expected this reply. The PhD is sufficient but not necessary here, right? A PhD researcher's job description is basically "learn necessary math, become a domain expert, and publish papers advancing that domain." It's difficult (but possible) to gain the same experience in industry if you don't have a graduate degree. Which company would pay you to work through Bishop or Goodfellow for a few months? Even a pri…

I would say PHD is sufficient to advance the field. That's no small thing, but only really overlaps at the start when just about anything advances the field and you need a broad focus. Machine leaning for sorting peas at high speed is a very well trodden area at this point with a lot of industry specific domain knowledge. I expect self driving cars for example to reach a similar state in ~10-25 years. The risk with a…

> I would say PHD is sufficient to advance the field.

Yep! We’ve now made our way back to my initial point in response to OP. :)

Re: Machine Learning Crash Course

#164

Post by taneq »

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…

> taking and passing a crash course doesn't mean you'll be an expert in the field (and this applies for most MOOCs, honestly)

Any field that you can become an expert in with a 6-week course or less is not a field that should be paying even high 5-figure salaries. Or, conversely, any field which pays 6-figure salaries is either not accessible via an MOOC, or is massively overinflated and probably in a bubble.

Re: Machine Learning Crash Course

#165

Post by [deleted] »

taneq wrote: post #164
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…

> taking and passing a crash course doesn't mean you'll be an expert in the field (and this applies for most MOOCs, honestly) Any field that you can become an expert in with a 6-week course or less is not a field that should be paying even high 5-figure salaries. Or, conversely, any field which pays 6-figure salaries is either not accessible via an MOOC, or is massively overinflated and probably in a bubble.

[deleted]

Re: Machine Learning Crash Course

#166

Post by bkanber »

redditmigrant wrote: post #114
bkanber wrote: post #84

Earlier quoted context omitted.

> How did you figure out what features would be useful? There are various feature engineering and feature extraction techniques. Filter methods, wrapper methods, and embedded methods. Principle component analysis, autoencoding, variance analysis, linear discriminant analysis, Gini index, genetic algorithms, etc -- the feature selection process will depend on the dataset, the problem domain, the analysis algorithm you…

Thanks for the response! > There are various feature engineering and feature extraction techniques. Filter methods, wrapper methods, and embedded methods. Principle component analysis, autoencoding, variance analysis, linear discriminant analysis, Gini index, genetic algorithms, etc -- the feature selection process will depend on the dataset, the problem domain, the analysis algorithm you ultimately use, etc. Obvious…

Every problem is different, so the only advice I can give is: research research research! Do the hard work up-front; figure out how to describe your problem in a mathematical sense, and identify the right tools to use for the shape of your input, output and problem dimensions. What's the distribution of each dimension. Are the relationships linear, nonlinear, clustered, dispersed, logarithmic, etc. Once you know those things, you're able to narrow in on the right tools and analyses to use.

Re: Machine Learning Crash Course

#167

Post by lovelearning »

suyash wrote: post #47

Is there something like this for Java programmers?

ML concepts are independent of programming language. I suggest not to let language preferences stand in the way of listening to lectures and understanding the subject.

For implementing exercises using Java, you have a bunch of good options:

1) The most direct equivalent to Pandas+Tensorflow I can think of is DL4J. They have a good comprehensive set of concept and implementation tutorials [1].

2) TF APIs have a Java port and can be used from java desktop and console applications [2]. So a second but slightly more difficult option is using TF Java port + Spark APIs.

[1]: https://deeplearning4j.org/documentation

[2]: https://www.tensorflow.org/install/install_java

Re: Machine Learning Crash Course

#168

Post by kmax12 »

fnl wrote: post #75
kmax12 wrote: post #13

Great to see they have a nice introductory section to feature engineering! Feature engineering is often the most impactful thing you can do to improve quality of models and a place where I often see beginners (and experts for that matter) get stuck. Google walks through how to work with json files and categorical variables https://developers.google.com/machine-learning/crash-course/... . If anyone is looking to get m…

Your comment got me interested in this course. However, all I could find about feature engineering there is what you linked to, directly. Given that entire scientific careers, books, and conferences are built around the topic of feature engineering, and at least IMO good ML tools live or die with good feature engineering (in its broadest sense, for you deep learning fanatics :-)) that doesn't seem like more than the…

10 seconds into the video of feature engineering they say that feature engineering takes up about 75% of the time https://developers.google.com/machine-learning/crash-course/...

They understand the value, but but if you keep watching, they don’t seen go beyond the basic.

Re: Machine Learning Crash Course

#169

Post by bkanber »

andyjohnson0 wrote: post #125
bkanber wrote: post #77

Earlier quoted context omitted.

I teach ML and am currently writing my 2nd book on it. I always advocate learning the fundamentals. Machine learning is math, and neural networks in particular rely on linear algebra and vector calculus. (You can build a NN without using linear algebra directly, likely it'll be slower and besides, the concept still relies on linear algebra). Frameworks abstract away a lot of the mathiness, which is a net good for soc…

Thanks for taking the time to write such a comprehensive reply - much appreciated. "ML is maths" is something that I'm getting used to now. I do have some real uses in mind for what I'm learning' both in my job and some side projects, particularly image feature recognition, and I'm looking forward so seeing how it all works in out. Thanks again!

Image feature recognition is not quite solved but I feel it's very close. It's easier, obviously, if the problem domain is very specific.

In the past, like when I started on ML, the best tip was to make sure to do some edge detection with a few convolutions before feeding an image to a neural network. Now, we have convolutional neural networks that kinda do that for you automatically.

Sometime in between those two dates, someone figured out how to get the convolutions trained via backpropagation -- and they did that by deriving the gradient of an arbitrary convolution (or more likely, looking it up). And that let us put convolutions right in the neural net and have the convolutions automatically train themselves along with the rest of the network. And we observe that the convolutions do things that we would do, like remove unnecessary detail and highlight edges or exaggerate colors.

Anyways; I believe the current state-of-the-art for generic image feature recognition is an ensemble of convolutional neural networks. I believe Google leads the pack on the commercial side so maybe look into how they do it.

Re: Machine Learning Crash Course

#170

Post by jimbokun »

bluetwo wrote: post #99
achompas wrote: post #59

Earlier quoted context omitted.

> 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…

I can't help but think in 3-5 years how quaint our tools of the day will seem.

I’m not so sure.

You can make an argument current tools haven’t really surpassed a Lisp Machine for developer productivity, or a SmallTalk environment.

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