Kelaberetiv

kəˈlabərətɪv/ 
adjective: collaborative

Bringing together the AI community in Singapore – companies, startups, researchers, students, professionals – to collaborate, find research and business opportunities and talent.

Have an account? Login
New user? Register
Forgot password? Reset

AI Singapore uses a 2FA login mechanism to protect your account. Learn more about it here.

Your Programmes:

Login to see the programmes are subscribed to.

Your Files:

Login to download your files.

Opportunities

  • Have an interesting story to share?
  • Seeking for AI talent for your organization?
  • Seeking research interns for your labs?
  • Seeking an industry partner for your AI projects?
  • Are you a researcher and seeking an industry partner to do a POC or deployment of your IP/research outcomes?

[Sticky] 10 things to know or do before you apply for the AI Apprenticeship Programme  

  RSS

Liyi Ang
(@liyi)
Member
Joined: 2 months ago
Posts: 35
October 12, 2018 10:50 am  

Here is a list of recommendations and resources for those who aspire to join the AI Apprenticeship programme or want a career in AI.

  1. Hone your Python and/or R skills. There are many excellent online resources. There is no need to go for a paid course (Seriously, if you need to attend a Python or R course to learn the language, then likely you are not the candidate we are seeking).
  2. Watch the excellent and legendary "Elements of Statistical Learning" series by Hastie and Tibshirani. See this post from R-bloggers
  3. Get familiar with Keras for deep learning (there is more to AI then DL, but this is a good start).
  4. Get familiar with Spark as a data platform for your AI/ML workloads
  5. Get an Azure (or AWS, GCP) account and go build data products in the cloud.
  6. Learn to package and distribute environments in Docker.
  7. Learn to manage a project with Git on GitHub, Bitbucket etc.!
  8. Concentrate on Numerical Linear Algebra and Statistical Computing because these power AI libraries today. Greenbaum and Chartier's textbook Numerical Methods is a nice theoretical introduction. For practical implementation, familiarise with the Tensorflow and Pytorch AND ALSO low-level Numpy, Scipy libraries. If you're at a more advanced level experiment with Numba and Cython more optimising how fast code runs.
  9. For a complete picture of what AI really is, nothing beats Artificial Intelligence: A Modern Approach by the gurus Peter Norvig and Stuart Russel.
  10. No projects from the office or idea what to build? Go to your local community and grassroots organization and offer to work on a project to help them build something! Until you build a real-world project, you will never experience the non-technical issues on the ground.

Happy learning!


kokann liked
ReplyQuote
Share:
Do NOT follow this link or you will be banned from the site!
  
Working

Please Login or Register