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CS246: Mining Massive Data Sets
- datlife 7y agoLove this course. Pretty rigorous, but the professor explains very well.
- senderista 7y agoI've done the class and can't recommend it highly enough (except for Ullman's lectures). I recommend the advanced track if it's still offered.
- fizwhiz 7y agoWhat's wrong with Ullman's lectures?
- master_yoda_1 7y agoWhat no deep learning, and nobody is claiming to solve all of your ranking problem in 5 lines of code ;) do we really need this course if we have fast ai ?
- benrbray 7y agoI think the ML hype is actually slowing down. I've been interviewing for ML positions as a new grad and a number of companies have told me they have an excess of data scientists who can train the models, but a dearth of engineers who can actually scale the models up to production. Friends at FAANGs have similar stories.
- mrlatinos 7y agoMy personal experience is that most of the models that data scientists are creating will never be scalable.
- NerdyDrone 7y agoThanks for sharing! As a new grad also looking for work, maybe I'll apply to more SE jobs, fewer data science-focused jobs lol. TBH applying for jobs is scary asf.
- pc86 7y agoMaybe it's just because I've been in industry for 10+ years now, but how are you qualified as a new grad to apply for both?
- tudelo 7y agoDouble major statistics + cs? masters statistics? Seems those would potentially qualify you if you are a solid candidate?
- benrbray 7y agoNot sure why you've been downvoted--it's really tough to know what skills will be marketable in 4+ years when choosing what to concentrate on in college. In my schooling, I really optimized for the math+stats background, since I enjoyed it and thought it would help me stand out. I even took a short detour into a machine learning PhD before deciding academia isn't for me and leaving with an MS. Now I'm on the job market, and although I have modest coding/engineering skills and a willingness to learn, it's tough to find a company willing to take the risk. Guess I min/maxed a little too much. Best of luck in your search!
- tudelo 7y agomy n=1 exp is that there are tons of openings for ML engineers but they are very wary of hiring them
- deleted 7y ago
- jph00 7y agoYou seem to have missed the entire point of the hundreds of hours of lessons provided by fast.ai, as a service to the community(+), which is this: to explain, and be able to re-implement from scratch, all the things that make those 5 lines of code possible. Some of that includes coverage of the probabilistic data structures and algorithms that are at the heart of the MMDS course. Along with computational linear algebra, analysis of the details of floating point representation, discussion of C/C++ interop, matrix calculus, parallel processing, Python accelerators like cython and numba, functional and object oriented programming, notation, and a lot more. Being able to solve challenging problems in 5 lines of code is much harder than solving the same problem in hundreds of lines of code. (+) fast.ai makes no money from any course - there are no ads, and everything is free. Why are some people so keen to stamp on those who volunteer their time to help others? Open source software development suffers the same problem.
- mcintyre1994 7y agoFor whatever it’s worth fast.ai is my favourite MOOC and the most impressive set of videos I’ve seen shared freely on the internet. Thankyou for everything you do!
- emmelaich 7y agoI have to give a small wry moue of amusement at "fast.ai" -- since an `ai` is a type of sloth.
- moultano 7y agoI wrote a guide to the Minhashing family of algorithms that goes into a little more depth than is covered in the LSH section of this course. https://moultano.wordpress.com/2018/11/08/minhashing-3kbzhsxyg4467-6/ https://moultano.wordpress.com/2018/11/08/minhashing-3kbzhsx... You might find it useful if you've ever thought about using MinHash and wondered how to incorporate weights rather than treating everything as a set.
- moultano 7y agoI periodically submit this to HN, but it never seems to get traction. https://news.ycombinator.com/item?id=22268810 https://news.ycombinator.com/item?id=22268810
- commandlinefan 7y agoI haven’t done the course, but +1 for the textbook. It’s freely downloadable and very readable. I learned a lot from it and the exercises are just the right level of difficulty to help you really internalize the material. I wish there were self-check answers in the “back” of the book, though.
- rahimnathwani 7y agoThis book? http://www.mmds.org/ http://www.mmds.org/
- datlife 7y agoYes
- abhgh 7y agoYes this is a good book, esp. for an overview. At one time, I used to organize weekly discussion sessions at work based on sections in the book :-)
- commandlinefan 7y ago> for an overview I’ve read this before - that MMDS is more of a survey of machine learning’s “greatest hits” than a place to learn AI concepts. Out of curiosity, what would you recommend as something more beginner friendly?
- abhgh 7y agoI agree that MMDS is probably not a good introduction for ML- pretty idiosyncratic choice of topics for that. If you are fine with lectures, I always recommend Abu Mostafas lectures [1]; I think they have an extremely good balance of intuition and math, and I love his teaching style. Pretty underrated imho. Since these are a bit dated, they don't cover Deep Learning. For this, I like Andrew Ngs lectures [2]. Hugo Larochelle has a pretty extensive course on NNs too - I have viewed segments from it, and liked them[3]. Have heard good recommendations for fast.ai [4] if you need hands-on experience. [1] http://work.caltech.edu/lectures.html [2] sample playlist - https://www.youtube.com/watch?v=UNmqTiOnRfg&list=PLFKog8qYYq0Fs6lQf0jOuQD4XUQWYANPy [3] https://www.youtube.com/watch?v=SGZ6BttHMPw&list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH [4] https://www.fast.ai/
- iagovar 7y agoOne day someone has to make a dataset of how much talent we waste in the EU. Bsc gets paid by EU taxes, guy ends up teaching in US university or working for US company. It has become the cycle of life.
- oarabbus_ 7y agoPerhaps once the EU pays more than 50% of the salary that is paid in the US to software devs/analysts/etc, we'll see this behavior change.
- tasubotadas 7y agoIronically, the EU has only few top tier software development companies because of (my personal uninformed opinion) all the taxes that reach 50% of the revenue to support social policies that include access to the universities.
- Eldt 7y agoOne problem I see here is considering it to be wasteful...
- gota 7y agoIt is wasteful from the perspective of the taxpayers that funded that highly-specialized, highly-sought after education only to see it move away (BTW the argument is the same even if that education was not strictly paid off by public money. The way societies and nations organize themselves, you're taking up a valuable resource just by occupying the "slot", even if you are paying for it the people around you are incurring in all sorts od externalities to support your existence and studies) I'm honestly surprised that the position that Brain Drain is not a problem exists and would be curious to see your reasoning Also - Brain Drain also negatively affects the destination country in the sense that it "eases" the societal pressure on providing top notch/decent education to the general population. Why bother with educating your people when you can let the best people from elsewhere immigrate? Revert that and see how fast FAANG backed education reforms hit Note -I'm not all in for either side, and believe as in most things there's an ideal middle ground. Let some come. Send some away, too! There's a lot of value in the exchange. But check the list od instructors in the post's page: a tremendously hot topic in one of the best educational institutions in the world - and how many instructors are stereotypically "immigrants"? I'm leaning towards 100%
- diehunde 7y agoLooks very interesting. Too bad they don't have video lectures.
- radiowave 7y agoThe videos from a previous Coursera offering of (I think essentially) the same course are here: https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62...
- mrlatinos 7y agoThere's a link to lecture videos towards the top of the page - https://www.youtube.com/channel/UC_Oao2FYkLAUlUVkBfze4jg/videos https://www.youtube.com/channel/UC_Oao2FYkLAUlUVkBfze4jg/vid... Here's a different YouTube channel I found with the full course: https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV https://www.youtube.com/playlist?list=PLLssT5z_DsK9JDLcT8T62...
- koulvi 7y agoThey do have class video lectures available from 2019 (not from coursera) http://snap.stanford.edu/class/cs246-videos-2019/ http://snap.stanford.edu/class/cs246-videos-2019/ I really pray Stanford doesn't put them behind the paywall.
- tasubotadas 7y agoI've taken the MOOC version of this course and it was very poorly explained. I hope the content and lecturing has changed since 2013 because the content itself is really interesting. I wasn't impressed with the quality of the book as well. I did learn quite a few methods there (minhash) that I got to use later so thanks for that, but compared to MLPR, Learning from Data, or TESL books the quality of the former pales.
- modwilliam 7y agoI've taken a more recent version of this course and it seems that concepts were explained pretty well - particularly, the graphics in the slides were good at explaining the various algorithms step by step.
- greymalik 7y agoCan you clarify what the MLPR and TESL books refer to?
- tasubotadas 7y agoMachine Learning and Pattern Recognition The Elements of Statistical Learning
- 0x31a 7y agoThe title of Bishop's book is Pattern Recognition and Machine Learning.
- tasubotadas 7y agoYep.
- egl2019 7y agoMLPR and TESL, as much as I like them, won't help you much when you have massive amounts of data, i.e. too much to fit into memory.
- fnord77 7y agowish stanford would put videos online. But I guess then a lot fewer people would spend the $6000 for the class.
- mrlatinos 7y agohttps://news.ycombinator.com/item?id=22258662 https://news.ycombinator.com/item?id=22258662
- tmpz22 7y agoDidn't Berkley recently take down all their educational videos because they were sued for not providing them in a fully accessible format?
- koulvi 7y agohttp://snap.stanford.edu/class/cs246-videos-2019/ http://snap.stanford.edu/class/cs246-videos-2019/
- DrNuke 7y agoNew telescopes with crowdworked astronomy needing good algos to process gazillion images at yet-to-be-seen detail.
- streetcat1 7y agoThe problem with such courses is that "massive" tend to change every year in exponential manner. For example, if I can have a single machine with 32 cores and 1TB memory? what is massive in this context?
- moultano 7y agoThe datasets grow to meet the computing available to them. The things gathering the data themselves become more powerful, and so more of that data makes it downstream. I'd define "massive" data as anything where n^2 is too big, where "too big" is bigger than either my ram or my patience.
- JimmyAustin 7y agoI've heard everything from "it doesn't fit in Excel" to "it doesn't fit in ram for a standard dev's laptop (~10gb) to "it doesn't fit in ram in a decent sized EC2 instance (~250gb).
- moultano 7y agoI started worrying at one point that all the techniques I learned when I started my career for working with big data were becoming obsolete, but they aren't. What you needed to do before to make things possible is now needed to make it fast.
- Qu3tzal 7y agoIsn't it the same as before? If 4gb of data was too big because you had 2gb of RAM, then the methods used at that time are the same you would apply for a 500gb dataset that can't fit in a 250gb RAM machine, right? New issues appear when you have to analyze 2Tb with a 32gb RAM machine, but when the order of difference is the same, the issues and thus the answers are the same as before?
- streetcat1 7y ago
- deleted 7y ago[deleted]