Category: Data Science
-
Maximum a posteriori with Tensorflow
In the past, a method for parameter estimation was covered, named maximum likelihood estimation (MLE). If you want to know more about it, visit the following blog post. There is another parameter estimation method that it’s worth mentioning. It’s called maximum a posteriori, shortened by MAP. To see how that works we’ll revisit the Bayes’ […]
-
Reflections on working as data scientist
Working full-time for almost four years as data scientist, has helped me shape and clarify some of the misconceptions I had about this role and the buzz built around this field. I summarized them into ten points. Needless to say these are all totally subjective: In spite of being the sexiest job of the 21st […]
-
Expectation-Maximization algorithm for the masses
Expectation-Maximization (EM) algorithm is widely known for being used in clustering or segmentation problems. Despite its usefulness, not so many know how the algorithm works. In this post, I will present the internals of the EM algorithm. Then, I’ll walk you through an example where I’ll develop the EM algorithm from scratch. And finally, I’ll […]
-
Building a REST API with Tensorflow Serving (part 2)
This post is the second part of the tutorial of Tensorflow Serving in order to productionize Tensorflow objects and build a REST API to make calls to them. Part 1 is located here. Once these Tensorflow objects have been generated, it’s time to make them publicly available to everyone. By building a REST API around […]
-
Building a REST API with Tensorflow Serving (part 1)
What is Tensorflow Serving? One of the features that I personally think is undervalued from Tensorflow is the capability of serving Tensorflow models. At the moment of writing this post, the API that helps you do that is named Tensorflow Serving, and is part of the Tensorflow Extended ecosystem, or TFX for short. During the […]
-
Maximum likelihood estimation with Tensorflow
We are given a data set and we are told to model its distribution. But, how to proceed? A reasonable first step would be to plot in a histogram the distribution of values. After looking at the histogram, you recognize its shape. It looks like a Normal, or… maybe it’s a Gamma distribution? Maximum likelihood […]
-
Introduction to random variables for non-data scientists
Me: Hey, just took a pic in the library! Look how old and dusty are these books! You: Are these the oldest ones you found in there? Me: No, I didn’t inspect all of them, just picked those five. You: Look! In the background you can see plenty of them. How many books do you […]
the long run 