[A version of this post appears on the O’Reilly Radar blog.] The O’Reilly Data Show Podcast: David Blei, co-creator of one of the most popular tools in text mining and machine learning. I don’t remember when I first came across topic models, but I do remember being an early proponent of them in industry. IContinue reading “Topic Models: Past, Present, Future”
Author Archives: Ben Lorica
Time-turner: Strata San Jose 2015, day 2
[Our friends at Dato created an interesting content-based, Strata session recommender. Check it out here.] There are so many good talks happening at the same time that it’s impossible to not miss out on good sessions. But imagine I had a time-turner necklace and could actually “attend” 2 (maybe 3) sessions happening at the sameContinue reading “Time-turner: Strata San Jose 2015, day 2”
Time-turner: Strata San Jose 2015, day 1
[Our friends at Dato created an interesting content-based, Strata session recommender. Check it out here.] There are so many good talks happening at the same time that it’s impossible to not miss out on good sessions. But imagine I had a time-turner necklace and could actually “attend” 2 (maybe 3) sessions happening at the sameContinue reading “Time-turner: Strata San Jose 2015, day 1”
Hardcore Data Science: 2015 California
Ben Recht and I hosted another great edition of Hardcore Data Science yesterday. From the very first talk, the room was full, the audience was attentive, and the energy in the room was high. It remained that way throughout the day. This time around, I spent more time documenting the day on Twitter – enjoy!Continue reading “Hardcore Data Science: 2015 California”
Forecasting events, from disease outbreaks to sales to cancer research
[A version of this post appears on the O’Reilly Radar blog.] The O’Reilly Data Show Podcast: Kira Radinsky on predicting events using machine learning, NLP, and semantic analysis. Editor’s note: One of the more popular speakers at Strata + Hadoop World, Kira Radinsky was recently profiled in the new O’Reilly Radar report, Women in Data:Continue reading “Forecasting events, from disease outbreaks to sales to cancer research”
Ask Us Anything at Strata+Hadoop World
One of the most popular sessions at last year’s Strata+Hadoop World in Barcelona was Spark Camp. Midway through this sold-out immersion day, it occurred to me that having an additional Q&A session would be helpful to the attendees. With minimal prodding from Paco Nathan, the other instructors of Spark Camp gladly agreed and so weContinue reading “Ask Us Anything at Strata+Hadoop World”
Network structure and dynamics in online social systems
Understanding information cascades, viral content, and significant relationships. [A version of this post appears on the O’Reilly Radar blog.] I rarely work with social network data, but I’m familiar with the standard problems confronting data scientists who work in this area. These include questions pertaining to network structure, viral content, and the dynamics of informationContinue reading “Network structure and dynamics in online social systems”
The evolution of GraphLab
[A version of this post appears on the O’Reilly Radar blog.] Editor’s note: Carlos Guestrin will be part of the team teaching Large-scale Machine Learning Day at Strata + Hadoop World in San Jose. Visit the Strata + Hadoop World website for more information on the program. I only really started playing around with GraphLabContinue reading “The evolution of GraphLab”
Building and deploying large-scale machine learning pipelines
[A version of this post appears on the O’Reilly Radar blog.] There are many algorithms with implementations that scale to large data sets (this list includes matrix factorization, SVM, logistic regression, LASSO, and many others). In fact, machine learning experts are fond of pointing out: if you can pose your problem as a simple optimizationContinue reading “Building and deploying large-scale machine learning pipelines”
A brief look at data science’s past and future
[A version of this post appears on the O’Reilly Radar blog.] Back in 2008, when we were working on what became one of the first papers on big data technologies, one of our first visits was to LinkedIn’s new “data” team. Many of the members of that team went on to build interesting tools andContinue reading “A brief look at data science’s past and future”