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

MMS Founder

Article originally posted on Data Science Central. Visit Data Science Central


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:


  1. Tutorials and Github
  2. Leaders and networks
  3. Deep Learning papers
  4. Interview questions


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

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. The AV community also maintains Best machine learning github repositories and reddit threads

Pradeep Menon’s blog/tutorial on Data Science central another great resource from an introductory maths perspective


Leaders and Networks

The people listed in this section are leaders in Data Science but also in some cases, lead major communities. Hence, you can get posts from the communities also by following them.


Brandon Rohrer    I have known Brandon since his days at Microsoft and follow his work at Facebook. Brandon is always free with sharing very detailed insights – and this post from his Microsoft days still remains a favourite – Which algorithm family can answer my question


Vincent Granville     heads Data Science Central. DSC maintains a great repositories of resources and also a set of good introductory primers ex primer on GANs


Matthew Mayo  and Gregory Piatetsky-Shapiro    of kdnuggets one of the best known sites for Data Science

Favio vazquez and Randy lao  are both insightful and motivating often sharing individual experiences


Dr Kirk Borne  I have followed Kirk from his days at NASA and now with Booz Allen Hamilton. Kirk shares great findings on AI, ML and DL


Deep Learning papers

Deep learning papers take a special effort to track because Deep Learning changes rapidly, sometimes  radically so  

DL papers are hard to keep track of

Here are some resources I follow

Terry Taewoong Um   maintains a list of Awesome deep learning papers

There is a well known Reddit thread on What deep learning papers should I implement

eric-feuilleaubois  ,  Rudi Agovic , Dr Charles Martin    are all prolific and insightful with an emphasis on Deep Learning

Shlomo Kashani  posts about Deep learning in Bioinformatics


Interview questions

Interview questions can be a great resource to learn

Here is my favourite resource blog on machine learning interview questions

via Andriy burkov. Andriy needs a category on his own! A must follow for a wide range of AI / ML topics and also a great sense of humour and perspective


To conclude, this list is not complete. Happy to add any suggestions. But it’s a challenge to keep the list both concise and insightful

Image source: NHS Glasgow

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