Python Basics with Numpy

In a previous post I commented on waking up exactly at 05:00 AM. This morning I woke up around 04:10 AM. I guess there is some variation every day on how much sleep you get based on several factors (i.e., physical exhaustion, mental exhaustion, noise, temperature, food intake, among others). Continue reading “Python Basics with Numpy”

Broadcasting in Numpy

Broadcasting is a feature of Python and Numpy. When one is performing array operations, in some cases the shapes of the arguments do not match. The good and bad thing is that Python assumes what you want to happen and does it. In most cases the results are fine, but on occasions Python might do something that you are not expecting. This post discusses to some degree what is broadcasting. The idea is that we will be using it in a future post when doing some regressions for image recognition. Continue reading “Broadcasting in Numpy”

Numpy Vectorization – Revisited

It is the last Sunday in January 2019 and is relatively cold in the Twin Cities of Minneapolis and St. Paul. The computerized mercury scale indicates -12F not taking into account wind. As usual, get up before 05:00 AM and get in my first 2 hour block of Deep Work. I am in the process of reviewing the last course I took on neural networks and deep learning. Continue reading “Numpy Vectorization – Revisited”

Guava – Multimap

Not to be confused with the fruit, Guava is an open source, Java based library developed by Google. It provides utility methods for collections, caching, primitives support, concurrency, common annotations, string processing, I/O, and validations.

I have been experimenting and using the Google Guava library for a few months. Most of features are quite nice and useful (e.g., Multimaps). In this post I show how easy it is interact with multimaps. Continue reading “Guava – Multimap”