How can beginners in machine learning, who have finished their MOOCs in machine learning and deep learning, take it to the next level and get to the point of being able to read research papers & productively contribute in an industry?

Andrew Ng 

Andrew Ng, Co-founder of Coursera; Adjunct Professor of Stanford

Courses are a very very efficient way to learn, so starting there definitely makes sense. After finishing the ML MOOC ( and Deep learning specialization (, here’re some additional steps you can take:

  1. Follow leaders in ML on twitter to see what research papers/blog posts/etc. they’re excited about, and go read them too.
  2. Replicate others’ published results. This is a very effective but highly under-rated way to get good at ML. Having seen a lot of new Stanford PhD students grow to become great researchers, I can say confidently that replicating others’ results (not just reading the papers) is one of the most effective ways to see and make sure you understand the details of the latest algorithms. Many people jump too quickly into trying to invent something new, which is also worth doing, but is actually a slower way to learn and build up your foundation of knowledge.
  3. When you’ve read enough papers/blogs/etc. and replicated enough results, almost magically you’ll start to have your own opinions and your own ideas. When you do build something new, publish it in a paper or blog post and consider open-sourcing your code, and share it back out with the community! Hopefully this will help you get more feedback from the community, and further accelerate your learning.
  4. Participate in any other enrichment activities that help you learn, such online competitions, going to meetups, attending (or watching online videos of) good AI/ML/vision/NLP/speech/etc. conferences like ICML, NIPS and ICLR.
  5. Find friends to do this with. You can make a lot of progress by yourself, but having friends to bounce ideas off will help your learning and also make it more fun. If you have access to AI experts like professors, PhD students, or good researchers, talk to them too. Sometimes I’ve learned a ton from a 5 minute conversation with people like Geoff Hinton, Yoshua Bengio, Yann LeCun; but also from my PhD students at Stanford, team members, or engineers at the various companies I sometimes visit.
  6. Despite the importance of having friends to work with, if your friends disagree with your ideas, sometimes you should still implement it and try it out to see for yourself. Geoff Hinton also said something similar in his “Heroes of Deep Learning” interview.

Every world class ML researcher I know has spent a lot of solitary hours implementing algorithms, tuning hyperparameters, reading papers, and figuring out for themselves what does and doesn’t work. I still find this type of work fun, and hope you will too.


Andrew Ng: how to use ML, AI for work
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