fbpx
Adapting Machine Learning Algorithms to Novel Use Cases
Editor’s Note: Kirk will present his talk “Adapting Machine Learning Algorithms to Novel Use Cases” at ODSC West 2019. If there was a metric for success in the data science profession, it would require a multi-dimensional scoring model. This metric would cover a data scientist’s technical skills and talents,... Read more
7 Tips for Visual Search at Scale
Visual search is a rapidly emerging trend that is ideal for retail segments, such as fashion and home design, because they are largely driven by visual content, and style is often difficult to describe using text search alone. It is a topic of interest to data scientists, as evidenced... Read more
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. [Related Article: Why The New Era of Big Data... 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
Handling Missing Data in Python/Pandas
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
ODSC West 2018 Review: Have You Been to Machine Learning Mecca?
With ODSC West 2018 in the history logs, it didn’t disappoint. Conferences can be overblown, and usually in proportion to the number of buzzwords marketed in its runup. ODSC West wasn’t; I went home with a number of practical tricks I will apply to the machine learning problems that... Read more
Crash Course: Pool-Based Sampling in Active Learning
Active learning is a class of machine learning problems where labeled data isn’t available for supervised algorithms. Let’s take the classic setup as an example. Say we have pictures of birds and want to classify them by type, but the images don’t have labels for what kind of bird... Read more
Classic Regularization Techniques in Neural Networks
Neural networks are notoriously tricky to optimize. There isn’t a way to compute a global optimum for weight parameters, so we’re left fishing around in the dark for acceptable solutions while trying to ensure we don’t overfit the data. This is a quick overview of the most popular model regularization... Read more
Three Ways Researchers are Using Data Science for Good
Data experts have long identified marginalization and narrow-minded problem solving as some of the biggest challenges facing data science. When large technology enterprises only seek solutions to problems they face within their company and their communities, it exacerbates inequalities. But companies, nonprofits, and individuals across the globe are making... Read more
9 Reasons Why You Should Go To a Career Fair
Tech companies have been known to ask… interesting…questions during interviews, such as the famous “How many golf balls can you fit in a school bus?” (It’s 660,000, apparently.) Whether it’s at Google, Facebook, Apple, or another company hiring for a data science position, you can bet that the interview(s) will be... Read more