A model to help improve naval mines detection using an autoencoder for feature selection and a deep neural network for binary classification

Robotic Submarines for naval mine defense | Image by US Naval Institute

A Brief Introduction to The Problem

There’s no doubt that mines represent an important issue to the navigability of the oceans. There are many of them laying around since several decades ago. The cost of producing and laying a mine is usually between 0.5% and 10% of the cost of removing it, and it can take up to 200 times as long to clear a minefield as to lay it. There still exist some naval minefields dating back to the World War II, and will remain dangerous for many years, since they are too extensive and expensive to clear.

In the following paragraphs several deep learning…


A 2-Layer Convolutional Neural Network with Fashion MNIST dataset

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Dataset Handling

During this project we’ll be working with the MNIST Fashion dataset, a well know dataset which happens to come together as a toy example within the PyTorch library.

The Fashion-MNIST dataset is proposed as a more challenging replacement dataset for MNIST. It is a dataset comprised of 60,000 small square 28×28 pixel gray scale images of items of 10 types of clothing, such as shoes, t-shirts, dresses, and more.

You can find here the repo of this article, in case you want to follow the comments alongside the code. As a brief comment, the dataset images won’t be re-scaled, since…


Analyzing which economic variables are helping the most

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There is much criticism and controversy about what the most capitalist countries do and how they protect the environment. Some of those ideas are misguided or have inherent prejudices. Some others make focus in particular issues such as CO2 emissions or how centrally-based policies can help to achieve a greater success factor, overlooking the big picture.

However, take into consideration the property rights. Everyone who pollutes the air, conditions our right to live in a cleaner environment which in some cases is our shared property. It shall be noticed that there is always interested parts protecting the places where they…


Franco Manca Augusto de Nevrezé

Introducción

Luego de un primer período con bajos contagios y poca propagación a lo largo de Argentina, desde hace dos meses el COVID-19 viene pegando fuerte en el país. El Ministerio de Salud dispone desde el 15 de Mayo de un dataset con cada caso registrado por localidad. Es una base de datos bastante amplia que hemos decidido analizar, considerando en esta primer aproximación algunos factores que nos parecieron interesantes, tales como:

  • Contagios por rango etario
  • Predicciones en base a estudios previos
  • Diferencias entre datasets: Min de Salud vs. COVID Stats AR
  • Mapa con densidad…


Coursera Data Science Capstone Project

Introduction

In year 2010, there were 32,999 people killed, 3.9 million were injured, and 24 million vehicles were damaged in motor vehicle crashes in the United States. The economic costs of these crashes totaled $242 billion. Included in these losses are lost productivity, medical costs, legal and court costs, emergency service costs (EMS), insurance administration costs, congestion costs, property damage, and workplace losses. This represents a 1.6 percent of the $14.96 trillion real Gross Domestic Product for 2010.

The society as a whole — the accident victims and their families, their employers, insurance firms, emergency and health care personal and many…

Augusto de Nevrezé

I write about Data Science, AI, ML & DL. I’m electronics engineer. My motto: “Per Aspera Ad Astra”. Follow me in twtr @augusto_dn

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