Teaching TensorFlow for Deep Learning at Stanford University (TensorFlow Meets)

Key Takeaways

welcome everybody to tensorflow meets and this is our very first episode of this show tensorflow meets is a show where we meet with people in the community who are working with tensorflow today it's my honor to meet finally with chip she's teaching the famous cs20 course at Stanford I have to tell a story before we begin about how I met chip so I was studying and studying some AI in Stanford and there was a robotics course what was the instructor great instruction he's amazing super cool guy and the first homework assignment came in and I did not have a clue years ago was it that long okay and I just remember and it was like you had offered fit to help and like I was totally stuck with this thing I chipped kind of scribbled this thing on a napkin and scanned it and emailed it to me and I was like then suddenly I knew what was going on and a second I knew this girl was going to go places after that and now look at you you're teaching you just say that so as a tower actually still considering doing an English major because I came to Stanford as a as a writer before Stanford I was like trembling a little bit and then wrote a couple of books and I really liked writing but then I took this one CS course I see us on 6 a.m. I everyone s neighbor takes this 106a everybody yes right yes and he said like okay like you work here then you sort of like had to take this course and take you to the stand robotics right of the course doctors I did like see us more and more and yeah ya know you had written like travel books in Vietnam right I'd like to call them stories it was traveling for almost three years like in Asia Africa and South America and it'll books about people I met and what I saw and what I thought of their life it was a whole new world to me like that in so it's a journey from like travel and meeting people about life and then coming to study like an English major and then in CS and now teaching AI so tell us about the course I has an idea when I was a sophomore and it was doing one class natural language processing and the course used tensorflow I started digging into the library and they did it all during the summer I use it for my job at a startup it was pretty early of the tensile flow progress so there was not a lot of documentation okay I had a lot of trouble trying to figure out what you do and and then I look into the source code and get up and I said wow this is like such a huge library with so many amazing talks and why does like what did they know by it before maybe there should be a group we were can just meet and learn from each other and so I told you some of my professors Stanford and we have a course on this of flow and they were like we are too busy to teach it so you know this is your secondary of teaching it right now yeah how's it been like picked up by students do they like it um for me as a student I'm not I for me two pictures of course I need a faculty sponsorship and Chris Manning was a person who did it and he advertised a course on a very popular course that he was teaching so the first year was pretty overwhelming we got like 350 applications Wow I was only allowed to take 20 because I'm not allowed to like take more students than that nicely yeah things the feedback was a pretty positive and then Stanford let me do it again look at more students but I because I'm still carrying of food work roles as a student stand for so I don't think I'm I'm ready for like a lot of students so this year we have about a 40 okay yeah it takes me on average 20 hours a week one lecture there to lecture so doesn't mean 40 hours it's a full-time job that's just a lot of time a lot of the information that you've put together for the course is available to the public right your code is on github and stuff like that have you seen like any uptake from students or from people outside using your code or learning from your code I think it was surprised at how many uses so I guess use a github repo just shake it on and I was like wow do you know that's like they're like an average like 2,000 visitors I've only recently joined the tensorflow team and just in the last couple of months so I've been scouring the web for things to learn how am I gonna learn this how am I gonna learn that and almost every like site that I fall on well people always end up pointing back to your github repo so it's okay if you want to learn how to do like a linear regression look at chips codon if you want to learn how to do this look at chips code so it's just saying some of it is a bit embarrassing because now because this year when I'm preparing again I redirect them notes from last night oh my god is this you know like when it when you try to make a joke but it wasn't funny it just showed on the paper it was like oh my God why did I even do that it's it's a constant process of improving it's the same when you're doing developer advocacy work it's like sometimes like I look back on talks that I've done it like no those jokes didn't work I'm glad I get a chance to do it again so I can have a slightly more polished so say I'm a developer and like I'm a typical software developer right now and I really want to get into the AI machine learning and there's a there's a whole universe of technologies out there AI nml what advice would you give me I think it's a bit hard way to get advice okay I think of myself as somebody who's a starting I have been doing it for like a little bit two years now it's because it's such a nascent technology two years is a long time so your experience is very valuable so I think is it really depends on on what exactly you want to get into because yeah like MLA is very broad so you can either become a software developer so you can develop tools like as a flow or pie chart you can also be like apply research finishes when you apply this a new research into into production or I can just become a very pure researcher so I come up with new algorithms you know your new architectures so for me um are they make as for me I have always wanted to become a researcher so in fact that for that massive math is very important to me and Jay brow need to know a matrix-matrix multiplication yeah yeah so I think it would recommend that you nature gets up leadership brought back growl like shut up and dinner so me some probability okay background because everything is indeed you Bhushan in this and anything read a lot of papers so some advice that have got from people who are in the fail for a long time this is tell me to read a lot of papers like this tell me like in every morning check okay like whatever photo like most popular papers oh wow and yes dedication yeah just like review them I like scheme does the abstract she doesn't so know that they what I go up to in the field right and then take a lot of online courses there's so many online courses available for free and I yeah and after needs a paper you can add implement it yourself like yes you try because you cannot render standard paper until you actually like a man was a paper something about and also can look at how other people implement that um online just to compare like the way you approach a problem that's the magic of open source yeah and like as we're talking about early run like the open source that you've written for your course that's on github has been very valuable for me learning it as well as for lots of other people you just say thank ya you're welcome you're welcome so thank you so much for being on the show chip thank you so much for having me oh you're very welcome and we've learned so much and thanks everybody for watching this episode of tensorflow meets remember this show is about you it's a show for the community where we would love to have you on the show where tensorflow meets you and talks with you about what you do if you've got any questions for me if you've got any questions for chip please leave them in the comments below and if you want to be on the show if you're doing something interesting please get in touch with us and remember don't forget to hit that subscribe button for more great tensorflow content bye [Music]