Why Consumers Should Trust Companies with Their Data
Hugo Pinto is an asthmatic. He’s aware of the environmental triggers that can induce an asthma attack, but he wasn’t satisfied with the option that faces most asthmatics: wait until an attack happens, and treat the symptoms once it does. At a hackathon, Pinto teamed up with other developers... Read more
Layer-wise Relevance Propagation Means More Interpretable Deep Learning
Wojciech Samek is head of machine learning for Fraunhofer Heinrich Hertz Institute. At ODSC Europe 2018, he spoke about an active area of research in deep learning: interpretability. Samek launched his lecture with the following preface on the rising importance of interpretability of deep learning models: “In the last number of... Read more
How to Play Fantasy Sports Strategically (and Win)
Daily Fantasy Sports is a multibillion-dollar industry with millions of annual users. The Imperial College Business School’s Martin Haugh created a framework to best those users by modeling what they’ll do and constructing a team based on it. Haugh presented his research on how to play Fantasy sports strategically... Read more
Mail Processing with Deep Learning: A Case Study
Businesses increasingly delegate simple, boring, and repetitive tasks to artificial intelligence. In a case study, Alexandre Hubert — lead data scientist of software company Dataiku’s U.K. operations — worked on a team of three to automate mail processing with deep learning. At ODSC Europe 2018, Hubert detailed how his team... Read more
Olivier Blais of Moov AI on His Experience as a Speaker at ODSC West 2018
I am back from Open Data Science Conference (ODSC West) in California. What a blast! Not only was I able to present my talk on the democratization of AI, but I have learned a lot of very interesting stuff! I honestly am impressed by the projects and technologies presented... Read more
Alexandru Agachi of Empiric Capital on “Handling Missing Data in Python/Pandas” at ODSC Europe 2018
Key Takeaways: It’s important to describe missing data and the challenges it poses. You need to clarify a confusing terminology that further adds to the field’s complexity. You should take the time to review methods for handling missing data. You need to learn how to apply robust multiple imputation... Read more
Thomas Wiecki of Quantopian on ‘Minding the Gap’ Between Statistics and Machine Learning at ODSC Europe 2018
Key Takeaways: It’s important for data scientists to understand the so-called “gap” between statistics and machine learning, and how there actually is a lot of commonality between the two; it’s just a matter of how you look at things. PyMC3 is a very useful probabilistic programming framework for Python.... Read more
Active Learning: Your Model’s New Personal Trainer
First, some facts. Fact: active learning is not just another name for reinforcement learning; active learning is not a model; and no, active learning is not deep learning. What active learning is and why it may be an important component of your next machine learning project was the subject... Read more