# Generalized Linear Models in TensorFlow

Linear regression is by far, one of the most used statistical models in the industry. To the extent that many times it's used because... why the fuck not? But in the end: All models are wrong, but some are usefulGeorge E. P. Box This statement remarks there are neither good or bad models. It's what … Continue reading Generalized Linear Models in TensorFlow

# Summarizing 2020

Photo by Caryn on Pexels.com As the year 2020 comes to an end, I would like to review how the blog has been doing for the past 12 months, in terms of visits and consistency on publishing new content. My intention by posting this entry is twofold: Measuring progress by looking at the blog KPI's, … Continue reading Summarizing 2020

# 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' … Continue reading Maximum a posteriori with Tensorflow

# 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 … Continue reading Reflections on working as data scientist

# 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 … Continue reading Expectation-Maximization algorithm for the masses

# 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 … Continue reading Building a REST API with Tensorflow Serving (part 2)

# 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 … Continue reading Building a REST API with Tensorflow Serving (part 1)

# 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 … Continue reading Maximum likelihood estimation with Tensorflow

# Introduction to random variables for non-data scientists

Photo by Morgan Harris on Unsplash 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 … Continue reading Introduction to random variables for non-data scientists

# The Journey Begins

Thanks for joining me! To be yourself in a world that is constantly trying to make you something else is the greatest accomplishment. — Ralph Waldo Emerson