Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Machine Learning Crash Course

developers.google.com

Post reply on HN
Print view View original

201–210 of 233 posts

Re: Machine Learning Crash Course

#201

Post by andreagrandi »

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's the main reason why I decided to present a talk at the next PyCon Italy, as a very junior data scientist, to inspire other Python developers to learn some practical machine learning. If I could do it (and use it for a work project already) many other people can do (and no, I don't even have a degree in CS, just years of work experience)

Re: Machine Learning Crash Course

#202

Post by derivt »

gmac wrote: post #83
blackrock wrote: post #60

Earlier quoted context omitted.

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"?

"Type I" and "Type II" errors are some of the stupidest and most obfuscatory academic terminology ever invented, and (as an academic) I absolutely refuse to make the effort to learn which way round they go. Just call the bloody things what they are: false positives and false negatives. (Getting seriously OT now, but Kahneman does something annoyingly similar with his talk of "System 1" and "System 2" in Thinking Fast…

When you are designing an hypothesis test the term positive and negative are not so clear. For example you can test that mean weight of bags is greater than 5.0 kg or smaller than 5.0 kg, both test are different, and some times you can accept both greater and smaller than 5.0 kg. The philosophy of hypotesis test is not as clear as a standard tests for pregnancy. In other terms,in some cases the H0 hypothesis is symmetric (>= versus =<) and is not clear what a positive result should be, you have to state clearly what is the H0 hypothesis. In a pregnancy test everyone agree than H0 is that you are not pregnant, that is in my HO the semantic difference between Type 1 error and false negative.

Re: Machine Learning Crash Course

#203

Post by YeGoblynQueenne »

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.

I'm going to have to ask who exactly are those AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience".

Re: Machine Learning Crash Course

#204

Post by YeGoblynQueenne »

bhrgunatha wrote: post #171
achompas wrote: post #123

Earlier quoted context omitted.

I think about this constantly. Not to sound like I walked uphill in both directions back in my day or something, but I remember building models in numpy without pandas. It was tedious -- and that's just a nice API wrapping ndarrays!

> Not to sound like I walked uphill in both directions back in my day Local minima?

Most likely, gradient descent with momentum.

Re: Machine Learning Crash Course

#205

Post by abhishekjha »

lanewinfield wrote: post #40
qwerty456127 wrote: post #38

Cool! Does anybody also know a good blockchain crash course of similar kind so one could grok all the major buzzwords of today?

I loved this one a lot: https://anders.com/blockchain/

This is what got me a proper intuition for why a blockchain can be useful.

Re: Machine Learning Crash Course

#207

Post by abhishekjha »

johnx123-up wrote: post #183
andyjohnson0 wrote: post #71

This looks like a well put-together course, and a good way to learn TensorFlow. Keras and TensorFlow are top of my list of technologies to explore in the very near future. Is anyone here doing Andrew Ng's Machine Learning course [1]? I'm about half-way through and really enjoying it. I'm particularly appreciating that the programming exercises are done in MatLab/Octave, so I feel that I'm really understanding the fun…

I used to download all ML videos for offline ad hoc watching. But, this crash course videos cannot be downloaded using 'youtube-dl'. Any recommendations?

Where are the videos anyway? I don't see any play button or ay console to play the videos.

Re: Machine Learning Crash Course

#208

Post by opencl »

xamuel wrote: post #73
eanzenberg wrote: post #66

Earlier quoted context omitted.

Why? Most of that cruft is abstracted away, computation only gets cheaper over time (a world class AI rig cost ~30k, a decent one for 2k) and most applications of ML run on commodity hardware.

For one thing, it suggests that they are actually technicians, not the scientists they're selling themselves as. That's fine if you want a technician (and if they're charging technician's rates).

I think when it comes to ML the CS experts with limited statistics knowledge are the technicians and the statistics experts with limited CS knowledge are the scientists, not the other way around.

Re: Machine Learning Crash Course

#209

Post by dannypgh »

xamuel wrote: post #93
joshuamorton wrote: post #89

Earlier quoted context omitted.

Why does an ML-expert need to know the halting problem? Considering that ML is really a CS-oriented form of statistics, why would you expect a statistician to know CS theory?

Thinking more, it's the misleading names ("machine learning", "AI") that rustle my jimmies so much. Sure, you don't need to know the halting problem to approximately solve MNIST by fitting a million-parameter curve to a dataset. But you're misleading people if you're claiming to have any kind of insight into how computers can be made intelligent, or how computers can "learn", when you don't even know the halting prob…

I disagree. Frankly, for a lot of people and a lot of contexts, I don't think the halting problem is particularly important. You're using understanding of it as a shibboleth for exposure to common curricula about theoretical computation. But you can even know a lot about practical computation and not know anything about the halting problem. Curious: has your knowledge of the halting problem ever actually saved you time or effort in your work? If so, how?

Turing's work on the limitations of his machine are interesting, and I'm sure people with a deep understanding of them can advance the study of computation.

I think you're just being dismissive of skillsets which aren't your own. I think you're just bothered by the fact that AI and ML are being advanced more by people with more knowledge of linear algebra and statistics than computer science. And realize that it's the arrogant among them that will dismiss you as "just a technician."

Anyone who is looking down on either "scientists" or "technicians" should get over themselves.

Re: Machine Learning Crash Course

#210

Post by Mashimo »

noahha wrote: post #187

serving videos from youtube without alternatives doesn't make it accessible. proxying for identified videos esspecially videos from this crash course would be useful.

>serving videos from youtube without alternatives doesn't make it accessible.

Why is that?

Post reply on HN