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Yes! Deep learning is a young field. I have a B.S., for example, and have published work at Interspeech (speech recognition conference) and NIPS workshop (machi
by brandonb 10y ago
Yes! Deep learning is a young field. I have a B.S., for example, and have published work at Interspeech (speech recognition conference) and NIPS workshop (machine learning conference). You can too.
My advice:
* 80% of machine learning is software engineering. If you're a strong software engineer, with some good math foundations (calculus, probability, matrix algebra), you can make significant contributions.
* For deep learning, you can start with the crash course, but you'll eventually want to read a full textbook. If you read the new Goodfellow, Bengio, and Courville textbook and understand the details of the algorithms, then you'll be up to speed with the major ideas in deep learning today: http://www.deeplearningbook.org/
* Find a company with existing deep learning experts who need software help -- that's most companies applying deep learning.
* Read research papers. Some will be easy to read, some will be hard. If they're hard, read the references they cite. Eventually they'll get easier.
Let me know if you need help!
- ujal 10y agoFor those interested, here is the relevant Interspeech paper[1]. The NIPS paper seems to be not published yet. [1] https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36756.pdf https://static.googleusercontent.com/media/research.google.c...
- brandonb 10y agoThe NIPS workshop was Machine Learning for Health. The paper isn't out as a PDF since a more mature version of the work will be submitted to a medical journal: http://nipsml4hc.ws http://nipsml4hc.ws
- dorian-graph 10y ago* 80% of machine learning is software engineering. If you're a strong software engineer, with some good math foundations (calculus, probability, matrix algebra), you can make significant contributions. Do you have some examples of this? I'd love to have a look at some real world ones.
- tostitos1979 10y agoNot sure what example you want. If you are talking about code, take a look at the torch source code. It is pretty accessible.
- brandonb 10y agoNot sure if this is what you're looking for, but Andrew Ng touched on a lot of pragmatic parts of software engineering in machine learning teams at Deep Learning School: https://www.youtube.com/watch?v=F1ka6a13S9I&t=3380s https://www.youtube.com/watch?v=F1ka6a13S9I&t=3380s
- achompas 10y agoscikit-learn is a poster child for best practices in machine learning engineering.
- codesternews 10y agoYou yes you!! Your paper have lot of fancy math. It will kill a software engineer like me. How you did that?
- brandonb 10y agoThe math is more approachable than it may seem. My recommendation is to read textbooks (not online tutorials or crash courses), which will guide you through the mathematical concepts and notation. Each little bit builds on the previous one, so if you read a few pages a day, every day, then after a few months, then concepts that once seemed very fancy will start to become more intuitive since you've seen the parts arranged in a different way before.
- alexbanks 10y agoCan I PM you?
- brandonb 10y agoSure, my email is brandon@cardiogr.am.