# An experiment with awesome deep learning papers

There have been several lists of deep learning papers doing the rounds. Recently Terry Taewoong Um’s list of the top 100 awesome and most cited deep learning papers caught my eye. Deep learning is an exciting area and it’s moving fast. I’d like to know what’s in those 100 papers (thankfully, we have at least looked at *some* of them before), and I suspect many of you would too. The problem is, at five papers per week it would take 20 weeks of The Morning Paper to cover the list! That’s too long, and even if it wasn’t, it wouldn’t match my goals for diversity of content. Ideally I’d press pause on the world for a while, go take a bunch of math classes, read through all of these papers, and then be in a strong position to understand the 2017 intake. That’s not possible either, so we need a faster plan…

*This week on The Morning Paper therefore I’m trying something a little different: we’ll be working through some of the top 100 papers list in sections, covering multiple papers per day but with shorter reviews of each. Hopefully you’ll find it a good way to take on board a lot of research very quickly.*

From my perspective the results are mixed – I certainly covered a lot of ground (26 deep learning papers read and reviewed over the week), but I think I discovered my current limit for paper reviews per week in the process! (It happened to coincide with a a busy work week as well). Another consequence is that the posts are longer than my usual target length, coming in at about 15 mins reading time according to my editor’s toolbar. I fear that may be too long, even in condensed review format. Let me know what you think.

One thing’s for sure, for the following week I’m going back to just one paper per day thank you very much! (And we’ll be looking at something other than deep learning too 😉 ).

“Ideally I’d press pause on the world for a while, go take a bunch of math classes, read through all of these papers, and then be in a strong position to understand the 2017 intake.”

This is actually what I’m doing right now 🙂 I’ve been using Khan Academy for the math part, as a prerequisite for Andrew Ng’s course, but a lot of the papers are still very fascinating and informative without all the relevant math skills.

Awesome Adrian!

I follow this blog on a daily basis and I’m also a researcher working with DL. I’d be very please to see more DL-related content here.

warm regards from Brazil

Sounds like you need some NLP, references graph theory and deep learning analysis of the deep learning papers? Feed them their own dog food.

Is there a guide to “math background needed for deep learning”? I guess stats and linear algebra to start…

Hi Paul, There are several collections of “maths for deep learning” resources on the web. This one has a pretty nice overview: http://datascience.ibm.com/blog/the-mathematics-of-machine-learning/

Regards, Adrian.

Awesome. Thanks Adrian!