Head First Data Analysis
Introductory solid book on Data Analysis
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.
Deep Learning (book review)
by Goodfellow, Bengio and Courville
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.
Preventing an AI arms race: open research
Secrecy is the halt of humanity's progress
I’ve noticed a trend in the AI research field, and one I feel pretty proud to talk about.
Ciencia de datos ágil
Curso de Ernesto Mislej
(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.
A Novice's Introduction to Data Science
Guest post on Making Sense's blog
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.
How Google’s New AI Innovations Will Transform Retail
by Rae Steinbach, in cooperation with Y Media Labs
This is another great guest post, this time from Rae Steinbach. She is a graduate of Tufts University with a combined International Relations and Chinese degree. After spending time living and working abroad in China, she returned to NYC to pursue her career and continue curating quality content. Rae is passionate about travel, food, and writing, of course.
Her post talks about the impact that Google’s AI vision will have on retail businesses. Thanks Rae; thank you very much!
During 2017’s Google I/O, where developers from all around the world explore emerging technologies together, the company announced several new elements for both Google Home and Google Assistant. For instance, Google Assistant, now equipped with AI, will be able to provide relevant information about your environment by “seeing” it through the phone’s camera. You could just point the lens at a business you pass on the street, suddenly receiving information about its services, customer reviews, and more.
The future of work, aided by AI?
How machine learning algorithms may take on our work
I recently came across the article Using Artificial Intelligence to Augment Human Intelligence, by Shan Carter and Michael Nielsen. I’d like to tell you a bit about the ideas that this essay mentions, and a few interpretations of my own about them.
Worklogger
Automatic timesheet entry
As you may know, part of the daily responsibilities of the software-workers is to log their time. (And many other professions too, I’m sure.) This implies reporting time with a certain level of detail so that our managers (sometimes, ourselves) can properly bill each customer for the work done.
The problem with this is that it is a pretty repetitive task, and not only that, each customer will have requirements of their own, like using their own system for time tracking, to separate the work in tickets, to receive a summary by email, etc.
I created Worklogger to be a swiss-army-knife solution to these variables.
Correlation ≠ causation
But causation ⇒ correlation|
In my earlier post I explained how certain type of machine learning models, specifically neural networks, find the correlations between two sets of values. For predictive models, we feed correlated variables to train our models. However, sometimes, we don’t know if or how variables correlate, and part of the machine learning intelligence is to actually find that out.
Neural Networks can learn anything
A simple explanation on the basis of neural network learning
This is a question I’ve been recently asked, and I think it’s interesting enough to share about. A few people asked me how is it that machines can learn, and specifically, how is it that neural networks can learn to understand data that may be really complex. The goal of this article is not to give an in-depth explanation, but rather one that can be easily understood.