A tangible approach to an area of machine learning in full swing

Photo by Lyman Gerona on Unsplash

Context

Surely you have heard about reinforcement learning [1] or, if not, surely you have been in contact implicitly with one of its applications. In the middle of 2021, there are many areas in which it has gained special relevance, but perhaps those in which it is more present, and has meant a qualitative leap compared to other areas of machine learning, are games/video games and the robotics industry.

A clear example of this within the world of robotics is the advances made by the company Boston Dynamics [2], founded by Marc Raibert in 1992 and bought by Google in 2013…


A practical way to approach a Machine Learning project

Photo by Marvin Meyer on Unsplash

Introduction

We’ve all heard of Machine Learning and we have a slight idea of the advantages it provides or the problems it can help us solve, but do we make proper use of this “tool” or do we pervert its use sometimes?

Most of the time, these types of projects emerge as an imperative solution to an existing problem or proposal but we don’t always notice whether this is, perhaps, the answer that best fits that question. …


Surrounded by technology.

Not many years ago, talking about artificial intelligence implied evoking an image of viped automatons performing properly human tasks with greater rigor and efficiency than ourselves (C3PO in Star Wars); or on the other side, robots with capacities and intentions harmful to the existence of the human race (Terminator in Terminator, 1984). Thanks to the internet and technology development, this situation has changed and currently this area of ​​computing is especially important in diverse productive sectors.

Although the term first appeared in 1956, coined by John McCarthy, Marvin Minsky and Claude Shannon at the Dartmouth Conference, its evolution and applications…

Anxo Rey Masid

Data Scientist at Securitas Direct by Verisure

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