Friday Afternoon in London

My working trip to Cambridge has been postponed meaning no stop-by by London, I thought. But a week before I also thought whatever, let's fly to London for the weekend and meet my friend Janiv as it was planed. Flights Berlin-London are so cheap it will be a shame to not use them, so I did!

Thanks to a so rare strike in Berlin airport I had to take off from Hamburg airport, giving me the chance to visit this city - shortly - for the first time. First time if I don't count the crossing of Hamburg by car on my numerous trips France - Scandinavia in the past years.

The trip
Wake up early, run to catch the tram, jump into the train in Berlin to Hamburg and S-Bahn to the airport, shitty croissant, tasteless sandwich and bad coffee I'm in the plane. Almost empty I fall asleep as soon as we take off. Arrival in London, buy a transport card, take the train, change at Victoria, take the tube, leave at Highbury and Islington Station and wait for my friend facing the well named Islington Cock Tavern.

First things come first, we have a coffee and delicious toast at Maison d'Etre then direction South Bank with the idea to visit an exhibition about light and color in a place I forgot the name while just briefly remembering a poster in the subway I may have seen an hour before... We reach the possible place which obviously was closed with reopening announced for 2018... A beautiful failure.

A post shared by jeremie Gerhardt (@mrbonsoir) on Mar 10, 2017 at 5:17am PST

So we keep walking and talking. Tate Modern in our back we bravely take the Millennium bridge and continue our exploration in the direction we came from but from the other side of the Thames. At Saint Paul's Cathedral we turn left, pass King's College London, then Wellington street, Covent Garden, aiming for Soho and the Photographer's Gallery which is ok but not crazy neither.

Tea time passed beer time arrives
Museum done, time for a pint and it will be the Blue Post. It's 6pm-ish, we found place to seat on the banquette and two ales. We talk and talk and we bring our neighbor into out discussion while searching for the name of the place. Time flies, two more ales, peanuts, local stories about the place, David Bowie, tailors in London versus tailors in Italy later and out stomachs scream for more. Goodby to our new friend and direction a not too far sushi place, later followed by ice cream. Always good to have local friends with similar food interest or obsession.

A post shared by jeremie Gerhardt (@mrbonsoir) on Mar 10, 2017 at 1:10pm PST

About not being an expert as a data scientist and other tech stuffs

Last evening I did attend a joined Meetup from the Python User Berlin (PUB) and the Zalando Tech Event hosted by Zalando and offering talks on Natural Langage Processing (NLP). Both talks went well and gave two views on the topic: one on the state of the art of the tools for NLP using Python and a second more applied.

The discussions I add after while enjoying a club mate - la boisson des champions - were equally interesting. First of all I started discussing with a expert of NLP trying to explain why I joined this event and what was my link with NLP. In my very recent job experience at EyeEm I just touched the surface of NLP preparing data for Machine Learning (ML) using nltk together with WordNet, ImageNet. Actually I didn't do much of text analysis but batching word definition. In that experiment the text analysis will have come after this step and that's where semantic is jumping into the discussion. Because working with the word dictionary is one side of the problem: you have one word with its definition and often - at least with scientists or engineers - you are in the inverse configuration which is you having words when actually you want to extract a definition, an idea, an information... And I let you google automatic image tagging, deep learning.

After exchanging ideas and experiences about NLP I did continue seeping the offered mate with one Zalando employee. I was curious - as usual - to understand what it means to be a data scientist here. Because if the definition is very general - a data scientist works with data, we are not expert - it's interesting to see how many fields we - I'm one of those people - cover in our daily work. Using the same language - e.g. Python - we can go from signal processing, computer vision, image retrieval, NLP, how to deal with Databases - a year ago I wrote on the topic Databases and natural Langage graphs en stock - how to present your results to non expert by doing nice visualization and many more... So if we are not expert we need to be pretty fast I acquiring skills from various fields and/or use the appropriate tools.