Watch: Project Feels – Deep Text Models for Sentiment Analysis
This video discusses the use of active learning, deep learning, Bayesian inference, and causality in Project Feels. This project, developed by the Data Science Group at the New York Times, sought to predict how likely a given article was to evoke a range of emotions. Thus project crowdsourced data... Read more
Watch: A Breakthrough for Natural Language
Natural language is valuable, but it is complex. With a 1,000 word vocabulary, a 15-word sentence can easily express more than 1e30 (a 1 with 30 zeros) different ideas. Today’s natural language processing is trained to bucket a sentence into one of a few thousand categories–which also means it... Read more
Ensemble Models Demystified
Ensemble models give us excellent performance and work in a wide variety of problems. They’re easier to train than other types of techniques, requiring less data with better results. In machine learning, ensemble models are the norm. Even if you aren’t using them, your competitors are. Kevin Lemagnen is... Read more
The Complete Guide to Decision Trees (part 1)
In the beginning, learning Machine Learning (ML) can be intimidating. Terms like “Gradient Descent”, “Latent Dirichlet Allocation” or “Convolutional Layer” can scare lots of people. But there are friendly ways of getting into the discipline, and I think starting with this guide to decision trees is a wise decision.... Read more
Watch: Applications of Deep Learning in Aerospace
Recent advances in machine learning techniques such as deep learning (DL) have rejuvenated data-driven analysis in aerospace and integrated building systems. DL algorithms have been successful due to the presence of large volumes of data and its ability to learn the features during the learning process. The performance improvement... Read more
Known Unknowns: Designing Uncertainty Into the AI-Powered System
Uncertainty may be a fearful state for many people, but for data scientists and developers training the next wave of AI, uncertainty may be a good thing. Designing uncertainty directly into the system could help AI focus on what experts need to leverage state of the art AI and... Read more
What are MLOps and Why Does it Matter?
During the industrial revolution, the rise of physical machines required organizations to systematize, forming factories, assembly lines, and everything we know about automated manufacturing. During the first tech boom, Agile systems helped organizations operationalize the product lifecycle, paving the way for continuous innovation by clearing waste and automating processes... Read more
Building a Scraper Using Browser Automation
Learning to scrape websites for data is essential to becoming a great data scientist. If the data you want to work with isn’t readily available, there’s always a solution, and collecting the data yourself is one of them. There are several ways to go about this—some websites have API... Read more
The State of Automatic Text Summarization with NLP
Ideally, NLP will be able to help humans complete tedious text-evaluation tasks, and its potential for use in fields like law and medicine have elicited significant enthusiasm. But where NLP has been applied to processes that do not align with strict mathematical evaluation, which perhaps require judgments of value,... Read more
Intro to Language Processing with the NLTK
Hidden information often lies deep within the boundaries of what we can perceive with our eyes and our ears. Some look to data for that purpose, and most of the time, data can tell us more than we thought was imaginable. But sometimes data might not be clear cut... Read more