For the past few months, I’ve been playing Madden NFL 17 in my free time. I really enjoy the team-building aspect of the franchise mode, where I’ve taken on challenges such as finally bringing the Lombardi trophy home to Philadelphia.

Another year, and another visit to CES in the bag. Here are my notes.

Suppose you work for a business that has paying customers. You want to know how much money your customers are likely to spend to inform decisions on customer acquisition and retention budgets. You’ve done a bit of research, and discovered that the figure you want to calculate is commonly called the customer lifetime value. You google the term, and end up on a page with ten results (and probably some ads). How many of those results contain useful, non-misleading information? As of early 2017, fewer than half. Why is that? How can it be that after nearly 20 years of existence, Google still surfaces misleading information for common search terms? And how can you calculate your customer lifetime value correctly, avoiding the traps set up by clever search engine marketers? Read on to find out!

Every so often, data scientists who are thinking about going off on their own will email me with questions about my year of freelancing (2015). In my most recent response, I was a little more detailed than usual, so I figured it’d make sense as a blog post too.

Word embeddings are great. They allow us to represent words as distributed vectors, such that semantically and functionally similar words have similar representations. Having similar vectors means these words also behave similarly in the model, which is what we want for good generalisation properties.

After my last post on analysing publication patterns I received quite a lot of feedback and many feature requests, so I decided to create an update once 2016 is over. It is now quite a bit bigger than before, and includes 11 different conferences and journals: ACL, EACL, NAACL, EMNLP, COLING, CL, TACL, CoNLL, *Sem+SemEval, NIPS, and ICML.

** Tue 03 January 2017

And yet again, it’s that time of the year when one eats too much and gets in a reflective mood! 2016 is nearly over, and us bloggers here at opiateforthemass.es thought it would be nice to argue endlessly which R package was the best/neatest/most fun/most useful/most whatever in this year! By now, it’s become a tradition.

A few months ago I attempted to understand the natural gradient, and wrote a post to help organize what I knew. Unfortunately there was too little detail and all I really understood was a “black box” version of the natural gradient: what it did, not how it worked on the inside.