I just exported my Wordpress blog by using the jekyll-import tool. This is my first post in this new tool. I very much like the simplified approach of writing markdown and having a static site rather than running a server and a database on my own.
Not all things were entirely straightforward, so here’s a quick list of the steps I took, which might be helfpul to others.
I recently participated in a meetup with the promising title “Ethics in AI”. Dr. Chris McKillop conducted the meetup, and she did not only has a lot of theoretical background under her arm, but also a great deal of experience with working on the field of Data Science and AI.
Most blogs and manuals will recommend you the simpler approaches to reducing the image of your docker image. We’ll go a little further today but let’s reiterate them anyway:
- Use the reduced version of base images (alpine usually recommended), avoid SDKs for final images
- Use multistage build, do not copy over temporary files or sources
- Take care of the .dockerignore, ignore as much as possible
Having said that, it is possible that you’ll still end up with a very huge docker image, and it’s difficult to understand what the next step from here.
This is where this post comes in.
I just finished reading Working effectively with legacy code, by Michael Feathers. The title is perfectly descriptive and quite ambiguous at the same time. Let me explain to you why.
Text generation with nltk, markovify, Tumblr, docker
(Image used without permission from Gunshow comic: Robot that screams.)
If the word offends you, below the fold I use it a lot, so you might not want to read this article. However, I think it’s the most appropriate term.
I just finished reading “Head First Data Analysis “, by Michael Milton, and it was the first book I’ve ever read from the “Head First” series.
The book was very easily approachable, with concepts introduced only when they were necessary and to make a good, valid, practical point. The whole structure of the book revolves around practice and “real life” examples (although, greatly simplified) to prove how methodical logical steps can naturally lead to a good analysis mechanism.
I finally finished reading Deep Learning, by Ian Goodfellow, Yoshua Bengio and Aaron Courville. And as a wonderful as a book as it is, it wasn’t an easy read, at all.
I’ve noticed a trend in the AI research field, and one I feel pretty proud to talk about.
(Post only in Spanish since I’m reviewing Spanish content. It’s a Udemy course talking about Data Science in an agile framework.)
Hace unos días terminé de participar del curso Ciencia de datos ágil, por Ernesto Mislej, co-fundador de 7puentes. Al comienzo me recomendaron el curso porque cubría un hueco que no estaba muy bien explicado en las fuentes online: cómo llevar adelante un proyecto de Data Science, fuera del típico proceso waterfall que siempre se describe. Además, Ernesto es una fuente de buena reputación, por lo que el curso me interesó.
I just wrote a guest post at Making Sense’s blog: A Novice’s Introduction to Data Science. Hopefully the first in a series, but for the moment, feel free to check that one out to find out what Data Science is and why you all the hype about it lately.