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Project Description

Automated generation of coherent text is an area of Natural Language Processing (NLP) that has garnered a great deal of attention over the past several years. Several state-of-the-art language models have been developed that are capable of automatically generating text at a quality that approaches that of human-generated text. The possibilities for automated text generation are endless, and while many potential use cases are seemingly benign (i.e. automated summarization of long texts, generation of sporting event recaps, generation of text for entertainment purposes, etc.), …


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Photo by Lee Campbell on Unsplash

Pitchfork is an online music review website that has been actively reviewing albums and individual songs since the mid-90's. The site started as a platform for reviewing independent, lo-fi, and underground artists that typically did not receive mainstream attention from mainstream music review publications. Over time, it gradually expanded to include reviews of more mainstream releases as well as classic album reissues. It still maintains a reputation as both a tastemaker and a bastion of musical snobbery, although currently it more closely resembles a traditional music review platform than it did in its early days.

The site gained popularity in the early 2000’s due to the unique and polarizing writing style presented in its album reviews. Reviews are often strongly opinionated, heaping glowing praise and flowery language on albums the writers liked and tearing apart albums the writers disliked. As readership grew, the site gained enough clout to help launch the careers of a large number of indie artists, including Arcade Fire, Sufjan Stevens, Bon Iver, The Decemberists, and countless other bands. …


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Photo by Eilis Garvey on Unsplash

This article provides a basic introduction to audio classification using deep learning. We will build a Convolutional Neural Network (CNN) that takes Mel spectrograms generated from the UrbanSound8K dataset as input and attempts to classify each audio file based on human annotations of the files. Code for this article can be found in this Git repository.

Audio Classification

Audio classification describes the process of using machine learning algorithms to analyze raw audio data and identify the type of audio data that is present. In most applications, this is done using annotated data with target classes selected by human listeners.

There is a wide range of different applications for audio classification. A great deal of research has been completed for speech recognition and development of speech-to-text systems. Audio classification has also been used for automated music categorization and recommendation engines. Classification of environmental sounds has been proposed for identification of specific species of birds and whales. Additionally, monitoring of environmental sounds in urban environments has been proposed to aid in law enforcement through identification of sounds that may be associated with crime (i.e. gunshots) or unauthorized construction (i.e. jackhammers). …


An Introduction to Time Series Analysis and Forecasting Using Python

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Photo by Matthew Henry on Unsplash

Time Series Analysis & Forecasting

Time series data refers to a set of observations collected at different points in time, often at a regular interval. Analysis of time series data is crucial in a wide array of industries, including finance, epidemiology, meteorology, social sciences and many others.

In this article, we will look at the following topics within the domain of time series analysis and forecasting:

  • Exploratory Analysis
  • Visualizations
  • Seasonal Decomposition
  • Stationarity
  • ARIMA Models

These topics provide a very basic introduction to time series analysis and forecasting. More advanced forecasting methods will be discussed in Part 2 of this article. …


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Photo by Eduardo Santos on Unsplash

Project Overview

The use of machine learning and artificial intelligence for detection and prevention of crimes has increased dramatically over the past few decades. Law enforcement agencies have access to large volumes of crime data stretching back decades, and they are looking for ways that this data can be leveraged for prediction of crime patterns and types.

This project focuses on the use of historical crime data in San Francisco to predict the category of a crime event given only information about the event’s time and location. The project is based on the San Francisco Crime Classification Kaggle Competition, which concluded in 2016. …

About

Scott Duda

Water/Wastewater Engineer ♦ Data Nerd

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