PhD student in Aerospace Engineering at the University of Cincinnati. Master’s degree Physicist, Data Scientist, professional google-searcher.

# Noise cancellation with Python and Fourier Transform

## Here’s how to use a very simple tool like Fourier Transform to obtain efficient noise cancellation, with few lines of code.

Modelling the noise is a really hard task. And I’m not even trying to argue with that. As a physicist, I deal with noise all the times, and it is not easy to capture it, model it, or deal with it in general.

For this reason, a lot of complex, smart, and efficient methods have been developed during the years. With the technological progresses that computer have experienced during the years, great performances of Machine (Deep) Learning de-noising algorithms have been obtained. …

# Reverse-Engineering water properties, with Machine Learning

## Here is how to understand where the water comes from, given its properties. With few lines of codes.

Machine Learning helps us a lot in everyday lives, and in ways sometimes we don’t even fully realize. The main principle (and the main reason why we use Machine Learning) is that when something is really hard to understand, we still might have a chance to solve it with data.

While it is true that ML is usually adopted to solve a problem,

another powerful option is to use Machine Learning to reverse-engineer.

But what does it actually mean?
Well, let’s say you create a new social media, and everytime someone posts something with a certain word (let’s say stupid)…

# Generating 3d Point Clouds of Geometric Shapes (spheres, pyramids and cubes), using Python

## Here’s how to generate your geometric shapes as clouds of points

One of the most promising technique of Computer Vision during the last 5 years is inarguably PointNet, and one of its greatest advantage is the ability of working with clouds of points rather than pixel-based data (a.k.a. images).

As this path seems promising and more and more data scientists and researchers are deepening it, we need to be able to deal with Point Clouds.

In this article, I will show you something that would have been extremely helpful for me, as I wrote almost the entire code by myself.

# Tokyo 2020 Tweets Topic Modelling with Machine Learning, using Python

## Here’s how to categorize Tokyo 2020 Tweets by their topic using Machine Learning.

As it was predictable, for two weeks it has been all about Olympics.

A lot of great stories have been made, a lot of stars were born and a lot of incredible events have happened. Moreover, in the era of social media “if you didn’t post it, it never happened”. For this reason, a lot of people tweeted about the Olympics, sharing their feelings and thoughts.

Our goal is to model the topics of the tweets. This means that, given a tweet as input, we want to automatically understand the topic of the given tweet.

Here are some examples with…

# Hands-On Climate Time Series Classification with Deep Learning, using Python

## Here’s how to build a Deep Neural Network for Time Series Classification, with few lines of code

Time series are a huge part of our lives. Basically everything can be modelled as a certain quantity (on the y axis) that varies as the time increases (on the x axis).

On the other hand, classification is an important application of Machine Learning. In fact, it is easy to consider lots of our goals as a classification task.

Combining these two things together we have time series classification. Our goal is easy to be determined:

We want a model that, given a time series (i.e. a quantity that varies as the time increases), is able to output a class.

# Using Artificial Intelligence to predict the spread of wildfires, with Python

## Here’s a step by step tutorial about how to use Data Analysis and Machine Learning to predict the spread of wildfires

During the last several days, terrible wildfires have spread all over Sardinia, Italy and all over the world. The images and videos are heartbreaking. When the summer season starts, these events tend to appear more often, and the final results are catastrophic.

One of the reasons why I decided to learn Artificial Intelligence (AI) was the idea of being directly able to help people and solve real world problems. I think this is one of them.

Let’s get started.

1The libraries:

# This is what happened after my Bachelor’s Degree in Physics

## And it was not planed…at all

So this was me in 2016 and this was the day of my oral examination, in High School. I was so, so nervous. :)

I remember that I was trying to be the best student I can and at the end I felt like I was generally prepared on every subject. Nonetheless, by best shot was unequivocally Physics.

If I think about 5 years ago, I had really confused and vague ideas about the work of a physicist, its research, its career opportunities. I just liked studying Physics, a lot. …

# Bitcoin Time Series (long-time) forecasting using Machine Learning

## Here’s how to use basic Machine Learning to forecast Bitcoin Stock Price

Note! I’m not a Bitcoin expert. This use case is just meant to show how to apply Machine Learning in a simple way and try to solve a real world problem.

When I started Machine Learning, I got fascinated by the idea of being able to interpret data and extract information out of them even if I had no prior domain knowledge.

In this case, as I’m a Physicist and a PhD Student in Aerospace Engineering, I have no idea or opinion about Bitcoin. Nonetheless, this topic is extremely discussed and it is interesting to understand how and when (and…

# Stephen King Text Generation with Artificial Intelligence (RNN), Using Python

## Here’s how I trained a Deep Learning architecture to write a Stephen King-like text.

I love reading, and I’m a big fan of Stephen King. Also, I’m a physicist and a data scientist, and I’m currently taking a break after I got my Master’s Degree. During this pause, I’ve decided to read another Stephen King book, and I really loved it.

I would never, ever, EVER, let a computer write a Stephen King book for one simple reason.

We are not there with the technology yet.

If you look at some NLP experiment (e.g. AI writing jokes here) you can safely say that we are not as good as humans in writing.

So you…

# Voice classification using Deep Learning, with Python

## Here’s how to use Deep Learning to classify the voices of an audio track

Sometimes humans are able to do certain kind of stuff very easily, but they are not able to properly describe how they do it. For example, we are able to clearly distinguish two different voices when they speak, but it is hard to describe the exact features that we use to distinguish them. As the task is hard to describe, it is even harder to teach a computer how to do it. Fortunately, we have data, and we can use the examples to train our machines.

Let’s get started!

## 1. The Setup

As the title say, I’ve used Python. …

## Piero Paialunga

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