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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:
- Tutorials and Github
- Leaders and networks
- Deep Learning papers
- 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
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
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
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
There is a well known Reddit thread on What deep learning papers should I implement
Shlomo Kashani posts about Deep learning in Bioinformatics
Interview questions can be a great resource to learn
Here is my favourite resource semanti.ca 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