Andrew Ng Machine Learning Career Advice

hello everyone my name is Jared Beckwith if you don't know me I'm on a journey of self studying artificial intelligence machine learning and deep learning and in today's video I'm sharing a clip from MIT professor Lex Friedman's YouTube channel where he posted a lecture from Andrew Inge giving a talk on applying the nuts and bolts of machine learning now Andrew tang he's a Stanford professor he founded co-founded Coursera co-founded Google brain super smart dude he pretty much ran the artificial intelligence lab at Stanford so anything you want to know about artificial intelligence machine learning deep learning he's the guy you want to go to and now without further ado let's get into the videos question where Andrew Inc answers how do you build a career in machine learning make sure you guys like this video and subscribe for more and let's get into the lecture you know I found that the number one question I get asked is how do you build a career in machine learning right and I think you know when I when I did a reddit ask me anything a reddit AMA that was one of the questions that was asked even today a few people came up to me and said you know I've taken a machine learning course it's a machine learning move from Coursera or something else what advice do you have for building a career in machine learning I have to admit I don't have an amazing answer to that but since I get asked that so often and because I really want to think what would be the most useful content to you I I thought I'll at least attempt an answer even though it's maybe not a great one right so this is the last thing I had at the stop is the kind of personal advice you know I think that I was asking myself the same question like a couple months ago right which is you know after you've taken a machine learning course what's the next step for developing your machine learning career and at that time I thought the best thing would be if you're 10 deep learning school Sammy Peter and I got to go to do this I hope it's really part of motivation um and then and then beyond that right the things that really help so by to have had actually I think all of our organizations in fact quite a lot of people want to move from non machine learning into machine learning and when I look at the career paths you know one common thing is after taking these courses to work on a project by yourself right I've seen I've loved respectful character a lot of people I should pass spending category blocks there and then and then become better and get surrounded um but I want a share of you one other things I haven't really shared oh by the way almost everything I talked about today this is this new content that never presented before right so I don't know I hope it worked okay thank you so I want to share of you really the the want to think of as a PhD student process right which is you know a lot of them people when I was teaching full-time at Stanford a lot of people joined Stanford and asked me you know how do I become a machine learning researcher how do I have my own ideas on how to push the bleeding edge of machine learning and whether you know you're working robotic so machine learning or something else right there's one PhD student process that I find has been incredibly reliable and and and I'm gonna say it and you may or may not trust it but I've seen this work so reliably so many times that I hope you take my word for it that this process reliably turns non machine learning researchers into you know very good machine learning researchers which is and there's no magic really read a lot of papers and work on replicating results and I think that the human brain is a remarkable device you know people often ask me how do you have new ideas and I find it if you read enough papers and replicant enough results you will have new ideas on how to push for this through the odds right I I don't know how to I don't really I don't know how the human brain works but I've seen this be an incredibly reliable process meat enough papers and you know between 20 and 50 papers later and it's not one or two it's more like 20 or me 50 you will start to have your own ideas and this has been C Sammy's not being this head there's an incredibly reliable process right and then my other piece of advice is um so sometimes people ask me what where can a eye is like and I think some people have this picture that when we work on AI you know if I do or Google opening or whatever I think some people have this picture of us hanging out and these on every you know well lit rooms with natural plants in the background and we're all standing in front of a whiteboard discussing the future of humanity right and all of you know working on AI is not like that frankly almost all we do is dirty work so one place that I've seen people get tripped up is when they think work on AI is that future humanity stuff and shy away from the dirty work and dirty work means anything from going on the internet and downloading data and cleaning theater or downloading a piece of code and tuning parameters to see what happens or debugging your staff traits that figure out why the silly thing you know overflowed or optimizing the database or hacking the GPU kernel to make it faster or reading a paper and struggling to replicate the result at the end a lot of what we do comes down to dirty work and yes there are moments of inspiration but I've seen people already stall if they refuse to get into the dirty work so my advice to you is and actually another another place I've seen people stall is that they only do dirty work then then you can become great at data cleaning but but not also not become better and better at having your own moments of inspiration so one of the most reliable formulas I've seen is really if you do both of these you know dig into the dirty work like if you're if you if your team needs you to do some dirty work just go and do it but in parallel meet a lot of papers and I think the combination of these two is the most reliable format I've seen for producing green researchers so um I want to close with just one more story about this and I guess some of you may have heard me talk about the the the Saturday story right but um for those of you that want to advance your career machine learning you know next weekend you have a choice right next weekend you can either stay at home and watch TV or or you could do this right and it turns out this is much harder and the no short term we're also doing this right next weekend I think this weekend you guys are all doing great but next weekend if you spend next weekend studying reading papers referents there's no short term rewards if you go to work the following Monday your boss doesn't know what you did your peers didn't know what you did no one's gonna patch on the back and say good job you spent all weekend studying and realistically after working really really hard next weekend you're not actually that much better you have barely any better at your job so there's pretty much no reward for working really really hard all of next weekend but I think the secret to advancing your career is this if you do this not just a one weekend but do this for weekend off the weekend for a year you will become really good at this in fact almost everyone everyone I've worked at a standard that was close and became great at this you know everyone actually including me was addressed to them we all spent late nights you know hunched over like a neural net Tooting hybrid parameters trying to figure out why I wasn't working and it was that process of doing this not just one weekend the weekend after weekend that that allowed all of us really to how brains neural networks to learn the patterns that told us how to do this so I hope that you know even after this weekend you keep on spending the time to keep learning because I promise that if you do this for long enough you will become really really good idea so just a wrap-up you know I'm super excited a I of the Mickey dis analogy that AI is the new electricity right and what I mean is that just as a hundred years ago electricity transformed industry after industry electricity transform agriculture manufacturing transportation and communications I feel like those of you that are familiar with AI are now in a amazing position to go and transform not just one industry but potentially a ton of industries so I guess that by do I have a fun job trying to transform not just one industry but multiple industries but um I see that you know is very rare in the history of in human history where one person where someone like you can gain the skills and do the work to have such a huge impact on society I think in Silicon Valley the face changed the world is overused right you know every every Stanford undergrad says I want to change the world but for those of you that work in the eye I think that the path from what you do to actually having a big impact on a lot of people and hoping of all the people in transportation and healthcare and logistics in whatever is actually becoming clearer and clearer so so I hope that all of you will you know keep working hard even after this weekend and go do a bunch of cool stuff for Humanity thank you [Applause] thank you [Applause]

Channel: Jared Beckwith, R. EEG T.

Views: 5,200

Keywords: how to build a career in machine learning, how to build a career in data science, andrew ng nuts and bolts, andrew ng deep learning, andrew ng machine learning stanford, andrew ng lex fridman, andrew ng, andrew ng deep learning course 3, andrew ng deep learning course 2, andrew ng deep learning stanford, deep learning andrew ng, deep learning ai, deeplearning.ai andrew ng, deeplearning.ai andrew ng course 2, deeplearning.ai attention

Id: hkagmGAu74Y

Length: 10min 1sec (601 seconds)

Published: Thu Apr 09 2020