A Data Science Central Community
Andrew Ng is a great fan of reading research papers as a long term investment in your own study (On Life, Creativity, And Failure about Andrew Ng). Anyone who has worked in our field (AI, Machine Learning) can attest to that. AI is a complex and a rapidly evolving field. It’s a challenge to stay up to date with the latest technical details.
Based on my experience, in this post, I discuss how you can stay up to date by learning from the community. From a personal perspective, I work in two niche areas – Enterprise AI and my teaching for AI and IoT at the University of Oxford.
My strategy for personal investment in my study is: to study a broad set of topics in the following four categories:
I have tried to create a concise list below which should give you depth for AI and Deep Learning. This list also reflects my personal study bias (for example Python) – hence is not comprehensive.
I am thankful to all the people/sources listed here for their willingness to share insights which have helped my own learning over the years.
Read the full list of resources (by Ajit Joakar), here.