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Machine Learning Crash Course

developers.google.com

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Re: Machine Learning Crash Course

#141

Post by xapata »

achompas wrote: post #62
eanzenberg wrote: post #34

Earlier quoted context omitted.

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.

> both technical, communicative, and even life skills that don't really exist for software devs Not a fan of this "data scientist is a unicorn" style of thinking. The best people in any profession (especially software engineering) also use these skills in their day-to-day work.

Data science isn't yet as stratified as software engineering, so there's less room for those without those "unicorn" skills. 10 years ago, there was no room at all. 10 years from now, there will probably be plenty of undergrads hired as junior data scientists.

Re: Machine Learning Crash Course

#142

Post by xapata »

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 guess "dynamic programming" must really bother you. That field was named completely arbitrarily, to secure funding.

The more you look around, the more you find science concepts are named for marketing purposes.

Heck, "data scientist" is a bit of nonsense.

Re: Machine Learning Crash Course

#143

Post by aoki »

joshuamorton wrote: post #89
xamuel wrote: post #64

Earlier quoted context omitted.

The other day I met with someone who was visiting my city to attend a big ML conference. In the course of our discussion, it transpired this person did not know the Halting Problem. He'd "heard of" Turing machines, but nothing more than "hearing" of them. Gatekeepers shouldn't keep gates just for gatekeeping sake. But if so-called ML experts don't even know undergraduate computer science, that should really give you…

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?

would you not expect your hypothesized (theoretical) ML expert to understand boosting, which is generally explained in terms of PAC learning, which draws on computational complexity?

that said, i'd also expect a phd in statistics to be able to figure out boosting without taking an undergrad course that worked up from automata. so the halting problem test, while it does capture something, may not be quite right.

Re: Machine Learning Crash Course

#145

Post by Puer »

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 real barrier to entry to ML is statistics. Most computer science degrees require an intro to statistics class, but if you really want to understand ML and where it should and can be applied appropriately you need a much deeper understanding.

IMO, it's much easier to pick up the programming required for ML than the statistics. This was reflected in the classes I took as a double statistics/computer science major. Most of the people in my CS department's machine learning course were statistics students looking to go into data science, not computer programmers looking to get in on the ML trend.

Re: Machine Learning Crash Course

#146

Post by mark_l_watson »

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…

Andrew Ng’s older machine learning MOOC class was excellent. I took it once, and took it again a few years later. In the last 8 months I also took his new deep learning set of courses. All really good stuf! (I have been working in the field since the 1980s, but constantly refreshing helps me. And Andrew’s lectures are great fun, he is a teaching artist.

Re: Machine Learning Crash Course

#147

Post by aoki »

eachro wrote: post #134
aoki wrote: post #120

Earlier quoted context omitted.

type ii error is statistics, not mathematics. there is no equivalent concept in CS because type ii error relates specifically to statistical inference and hypothesis testing. that said, if you are just pointing to a box in a confusion matrix and saying "TYPE II ERROR," you are probably trying too hard.

Eh, but if you've taken a machine learning course, you should have seen the notion of false positive/false negative when you cover any kind of classification technique.

but they're not actually equivalent, in spite of tables like this [0]. type ii error is a false negative result in the context of a test, where you have to understand which hypothesis is which and exactly what you are accepting or rejecting (hypotheses are not always as simple as hotdog/not-hotdog); if your listener doesn't know what statistical tests mean or wasn't following the setup, they have to stop you and ask.

[0] https://en.wikipedia.org/wiki/Type_I_and_type_II_errors#Tabl...

Re: Machine Learning Crash Course

#148

Post by radarsat1 »

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…

Whether you end up using a machine learning approach or hand-crafting the solution, I recommend you work in a ML-like manner, dividing up the data you have into test and training sets and using cross-validation to evaluate your work.

For you actual question, yes, as others have said it might be just an NLP/regexp problem. Otherwise, you could look at ingredients identification as a classification approach. I recommend checking FastText, NLTK, familiarize yourself with word dictionaries and pre-trained vectors that are available, these tools might help generalize your work beyond the data you have at hand.

(E.g. if it works well on your data using pre-trained word vectors from wikipedia, chances are it might work on examples you don't even have.)

Re: Machine Learning Crash Course

#149

Post by wepple »

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…

Take a look at some of the highly rated kernels on Kaggle - they’re often well annotated with the types of things you’re looking for, including actual experimentation to test ideas.

Edit: fix autocorrect

Re: Machine Learning Crash Course

#150

Post by tonypace »

p1esk wrote: post #102
icc97 wrote: post #48

Earlier quoted context omitted.

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 Neura…

Understanding NNs is easy. Understanding, collecting, and cleaning up data is the hard part. Also, DL != ML. Paraphrasing "The Tao of Network Protocols": If all you see is DL, you see nothing.

There are a tremendous number of people outside of programming who spend much or all of their work time collecting, cleaning up, and understanding data. Think teachers, accountants, traders - essentially everyone who spends a lot of time in spreadsheets.
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