How I Became a Machine Learning Practitioner
blog.gregbrockman.com
How I Became a Machine Learning Practitioner
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Re: How I Became a Machine Learning Practitioner
#2Given that Greg Brockman was the CTO of Stripe before OpenAI, that's a order of magnitude more technically/CS capable than the typical reader who might be looking into ML.
Re: How I Became a Machine Learning Practitioner
#3Given that Greg Brockman was the CTO of Stripe before OpenAI, that's a order of magnitude more technically/CS capable than the typical reader who might be looking into ML.
Studying Math at Harvard/MIT certainly puts you in a different category than the average software engineer. And if ML was still challenging to Greg, it is honestly a bit discouraging.
Re: How I Became a Machine Learning Practitioner
#4Given that Greg Brockman was the CTO of Stripe before OpenAI, that's a order of magnitude more technically/CS capable than the typical reader who might be looking into ML.
Studying Math at Harvard/MIT certainly puts you in a different category than the average software engineer. And if ML was still challenging to Greg, it is honestly a bit discouraging.
(I wrote the post.)
If it's helpful, I dropped out of both schools — the vast majority of my knowledge is self taught!
Re: How I Became a Machine Learning Practitioner
#5Earlier quoted context omitted.
Studying Math at Harvard/MIT certainly puts you in a different category than the average software engineer. And if ML was still challenging to Greg, it is honestly a bit discouraging.
(I wrote the post.) If it's helpful, I dropped out of both schools — the vast majority of my knowledge is self taught!
>I learn best when I have something specific in mind to build.
This is so incredibly important for me and, based on my conversations, many others as well.
The other thing I struggle with is the feeling that many of the problems I wish to solve are likely also solvable with simpler statistical methods and that I'm just being a poser by trying to pound them home with the ML hammer.
Re: How I Became a Machine Learning Practitioner
#6Given that Greg Brockman was the CTO of Stripe before OpenAI, that's a order of magnitude more technically/CS capable than the typical reader who might be looking into ML.
Studying Math at Harvard/MIT certainly puts you in a different category than the average software engineer. And if ML was still challenging to Greg, it is honestly a bit discouraging.
Maybe if you want to write tensorflow, but not if you want to use it.
Re: How I Became a Machine Learning Practitioner
#7Given that Greg Brockman was the CTO of Stripe before OpenAI, that's a order of magnitude more technically/CS capable than the typical reader who might be looking into ML.
Yes but the transition definitely doable and his advice is great. The key part of his advice is spending time experimenting, rapidly failing, and continuing to work on it with real world use case. Often the challenge is making the jump from the simple toy examples used in educational materials to the messiness of real-world data.
I'm a senior data scientist at vc-back startup. I'm in a hybrid data scientist/ machine learning engineering role, where I build and train ml and deep learning models and also build the scaffolding them to support their production usage. But my previous roles included being a business analyst, project manager, and research analyst. My undergrad education was in Creative Writing and the social sciences.
While I kind of accidentally transitioned into this career, how I got here is similar to most folks coming from a different background. Lot's of self-study and experimentation. I think one of the challenges to transitioning into ML and deep learning is that there are so many applications, domains, and input formats. It can be overwhelming to learn about vision, nlp, tabular, time-series and all other formats, applications and domains.
Things solidified for me when I found a space I found compelling and I was able to dive deep into it. You kind of learn the fundamentals along the way through experimentations and reflection. My pattern was pick up a model or architecture. Learn to apply it first to get familiar with it, experiment with different data, and then go back to build it from scratch to learn the fundamentals. That and I read a lot of papers related to problems I was interested in. After a while, I started developing intuitions around classes of problems and how to engage them (in DS you rarely ever solve the problem, there's always room to improve the model ...)
Re: How I Became a Machine Learning Practitioner
#8Given that Greg Brockman was the CTO of Stripe before OpenAI, that's a order of magnitude more technically/CS capable than the typical reader who might be looking into ML.
Studying Math at Harvard/MIT certainly puts you in a different category than the average software engineer. And if ML was still challenging to Greg, it is honestly a bit discouraging.
I think "doing" ML math is a little different than "doing" math math.
With math, on paper say, it is hard to tell if you are doing it right or wrong. You can trick yourself quite easily. A compelling proof can have a huge hole.
You can still trick yourself programming -- in a sense, that is what a bug is -- but it is much harder.
The upshot is, I think it is easier to teach yourself math that is applied to a computer program than math on a piece of paper.
Re: How I Became a Machine Learning Practitioner
#9Given that Greg Brockman was the CTO of Stripe before OpenAI, that's a order of magnitude more technically/CS capable than the typical reader who might be looking into ML.
Yes but the transition definitely doable and his advice is great. The key part of his advice is spending time experimenting, rapidly failing, and continuing to work on it with real world use case. Often the challenge is making the jump from the simple toy examples used in educational materials to the messiness of real-world data. I'm a senior data scientist at vc-back startup. I'm in a hybrid data scientist/ machine…
Thanks for the info.
I have a serious question (not for bashing)
Can you please describe what part of your job CANNOT be automated?
Re: How I Became a Machine Learning Practitioner
#10Given that Greg Brockman was the CTO of Stripe before OpenAI, that's a order of magnitude more technically/CS capable than the typical reader who might be looking into ML.
Studying Math at Harvard/MIT certainly puts you in a different category than the average software engineer. And if ML was still challenging to Greg, it is honestly a bit discouraging.
So there is a difference between ML research and application. Being a practitioner doesn't require deep math knowledge that perhaps research would. Jeremy Howard's fastai course is a great example of how someone with a solid programming background can effectively transition into being a deep learning practitioner. Given that production ml and deep learning is still the wild west, as a practioner, you can contribute also to the research around effective training, scaling, and application of these models. The math and intuition required are definitely acquirable.
I think when you shift into pure research, yes a deep probability, information theory, linear algebra, and calculus background are needed. But at the level, you're rarely writing code and more likely working at theoretical level.