The Power of Universal Latent Space In Medical Breakthroughs
The cost to develop new medicines has grown tremendously despite our computing and medical advances. Although we have a greater need than ever for massive breakthroughs in medical science, the industry itself is still a slow, highly regimented field. Mason Victors, CTO and CPO for Recursion Pharmaceuticals believes that... Read more
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
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
Watch: Unsupervised Feature Learning with Matrix Decomposition
Supervised learning is among the most powerful tools in data science but it requires a training dataset in which one knows the classes of the input features apriori. For example, a classification algorithm learns the identity of animals through training on a dataset of images that are labeled with... Read more
7 Steps to Go From Data Science to Data Ops
Not too long ago, data operation wasn’t on the radar, but now that it’s all people talk about, how can you move efficiently from data science to data ops? Gil Benghiat, co-founder of Data Kitchen, shares seven steps to do just that. [Related Article: The Difference Between Data Scientists... Read more
What are Some of the Best Practices for Hiring Data Scientists?
Instead of focusing on any particular issue or advancement relating to data science, Alluvium founder Drew Conway used his platform at ODSC to discuss an important issue relating to the field’s culture: how to hire data scientists for your company. Drawing from his extensive experience on both sides of... Read more
Introduction to R Shiny
Alyssa is a speaker for ODSC East 2019 this April 30 to May 3! Attend her talk “Data Visualization with R Shiny.” What is R Shiny? Shiny is an R package that enables you to build interactive web apps using both the statistical power of R and the interactivity... Read more
Linguistics In NLP: Why So Complex?
Natural language processing has many applications across both business and software development, but roadblocks in human language have made text challenging to analyze and replicate. Why can’t computers seem to get it exactly right? Mariana Romanyshyn from Grammarly sheds light on why and discusses what you need to know... Read more