How you can stay up to date with your #AI and #MachineLearning knowledge

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

Tutorials and Github are a terrific way to stay up to speed with code.

Here are my favourites

Sebastian Raschka  is a friend and has a great repository Sebastian Raschka  github

relating to one of the best known books on Python Machine Learning and Deep Learning with Python

Jason Brownlee when I started with data science, Jason’s books were my earliest resource. They are both easy to follow and detailed 

Towards Data Science The TDS community is a recent favourite. I also support them on patreon. They come up with a lot of detailed posts like Cheat sheet on reinforcement learning  federated learning

Manohar Swamynathan’s github repository based on his book Mastering Machine Learning Python in 6 easy steps  manohar swamynathan. I like this approach because its based on lots of small programs which you can use to learn. 

Analytics Vidhya is based out of India and produces a great set of resources.