Python List Comprehension support is great for creating readable but compact code for representing mathematical ideas. In this blog post, the concept of list, set and dictionary comprehensions are explained and a few examples in Python are given.

In this Python Matplotlib tutorial series, you will learn how to create and improve a plot in Python using pyplot. Matplotlib is a 2D plotting library written for Python. It consists of pyplot (in the code often shortened by “plt”), which is an object oriented interface to the plotting library. Matplotlib is an initiative of John Hunter, Darren Dale, Eric Firing, Michael Droettboom and the Matplotlib development team. Lets stop talking and start creating some beautiful plots using Matplotlib!

I’m planning to put this into Arxiv, but thought I’d share via a blog post first in case there’s any feedback.

I’m back at BudapestBI and this year it has its first PyDataBudapest track. Budapest is fun! I’ve had a second iteration talking on a slightly updated “Machine Learning Libraries You’d Wish You’d Known About” (updated from PyDataCardiff two weeks back). When I was here to give an opening keynote talk two years back the conference was a bit smaller, it has grown by +100 folk since then. There’s also a stronger emphasis on open source R and Python tools. As before, the quality of the members here is high – the conversations are great!

I’ve recently written a couple of guest blog posts about data science in the healthcare space.

If you’re wondering why this is part 4.1 instead of part 4, and why I’m not talking about continuing to build the local jbc, it’s because explaining Bitcoin’s Proof of Work difficulty at a somewhat lower level takes a lot of space. So unlike what this title says, this post in part 4 is not how to build a blockchain. It’s about how an existing blockchain is built.

As you can see, we have a variety of images. They all are in different resolutions. Cats and dogs are in different shapes, positions, colors. They may be sitting, they may not. They may be happy or sad. Cats may be sleeping, dogs may be barking. Photographs can be taken from diffrent angles with a different zoom.

As some of you already know there has been some movement at MSR lately, specifically for the theory group. We have now branched out into two groups, one in Machine Learning and Optimization -MLO- (with Zeyuan Allen-Zhu, myself, Ofer Dekel, Yuval Peres, Ilya Razenshteyn, Lin Xiao, and until recently postdoc Yin Tat Lee) and one in Algorithms (Nikhil Devanur, Sergey Yekhanin, postdocs Janardhan Kulkarni and Xiaorui Sun). The MLO group is part of the new MSR AI organization headed by Eric Horvitz, with grand goals at all levels of AIs (including a broad view of the foundations of AI which very much includes the type of mathematics showcased on this blog). Both groups are now hiring at all levels. From my point of view MSR is a unique place where you can devote all your time and energy to your research. The growth opportunities are truly amazing here: constant stream of world-class experts in algorithms, optimization, probability; lots of resources for travel, interns, postodcs; closeness to real-world products and possibilities to see your research directly applied. Below is the official call for application of the MLO group (the Algorithms’ one is here). Looking forward to receive your application!

Last week we crossed the one year mark after president’s election. Both in calendar days, November the 8th, and in terms of trading days – 252 (the average number of trading days per calendar year). I wanted to do a brief recap before leaving it to rest.