mitchellkoch / Mitchell Koch

There is one person in mitchellkoch’s collective.

Huffduffed (572)

  1. Building and deploying large-scale machine learning applications

    The O’Reilly Data Show Podcast: Danny Bickson on recommenders, data science, and applications of machine learning.In this episode of the O’Reilly Data Show, I spoke with Danny Bickson, co-founder and VP at Dato, and the principal organizer of the Data Science Summit (full disclosure: I’m a member of the conference organizing committee). Among machine learning students and practitioners, recommender systems have become somewhat of a canonical use case and application. One of the early and popular building blocks was GraphLab’s collaborative filtering toolkit, a library originally written and maintained by Bickson. He has continued to keep tabs on the latest developments in recommenders and continues to help organize workshops on related topics throughout the world. In recent years Bickson has turned his focus toward helping companies deploy machine learning systems in production in a wide range of real-world settings. Here are some highlights from our conversation:

    Building a toolkit for collaborative filtering

    It was kind of accidental. I was working on my Ph.D.—a lot of linear models, like linear systems of equations and interactive solvers. Matrix factorization, which is the base algorithm behind collaborative filtering, is very related to linear systems. It can be thought of as some kind of extension, and it’s more powerful. … When I was at CMU, I heard a lecture by a guy who’s now a researcher at Facebook, who actually worked on what they call Bayesian Tensor Factorization. This work drew me toward the domain, and I started to look into it. His code was in Matlab, so I tried to re-implement it on our system, GraphLab.

    Initially, when we started the project, we had what we call a framework, which is like an API for graph analytics. But we found out that not many people are interested in just writing code for a framework because it’s a very low level and it’s not that intuitive. … Once we started to package algorithms on top of the framework, then we became way more popular because people wanted to use pre-made building blocks.

    One of the reasons behind the success of this toolkit was that we started to compete in what was a relatively known competition called ACM KDD Cup. It was back in 2011. … When we started to compete using our code, we actually did something that was counter-intuitive: we shared our code during the competition, and then people, if they downloaded it, could improve their own results. That got us very quickly to hundreds of downloads, and a lot of companies were involved in this competition, so that opened a lot of doors for us in industry.

    Recommender systems

    The pillar stone of recommender systems research started with the Netflix competition, which, I guess, most of us know. … That was for movie recommenders. Their main assumptions were that you echoed information about user-to-movie interaction and their scores. That’s a kind of program that we are all very familiar with. There are hundreds of research papers. It is an explored domain where we are very good.

    The areas that need a bit more attention are those where you have additional data. … [where] you also know the day of the week, and the time, and which type of iPhone the user had, and what the user’s age and zip code are, what the item color and price is, and so on. Once you throw in more information, of course you can build richer models, but then the complexity goes up.

    … You can have models that rely on user behavior. You can have separate models that rely on activity data, like finding similar cars to the ones previously sold, and so on. There are models based on text description of products. We have models based on user reviews of products, text reviews, and sentiments. There are models that even take into account images of products. But the most interesting models are hybrid models that combine a lot of types of inputs because companies have very rich information. Currently, they’re using just a small fraction of that information to make the predictions, but once they gather more information they can have better models and more accurate models. That is what’s most interesting to me personally.

    Deep learning using Dato

    As you know, deep learning is one of the hottest techniques in machine learning, so we did want to have a foot in this domain. So far, we have an initial version, which supports convolutional neural networks. But the good news is that we hired some of the people behind MXNet, which is one of the emerging deep learning platforms, and there you have a lot of other algorithms, including RNN, and you also have features like support of multiple GPUs.

    Editor’s note: Danny Bickson will present a talk entitled New trends in recommender systems at Strata + Hadoop World London 2016.

    Related resources:

    The evolution of GraphLab (a previous episode of the Data Show featuring Dato co-founder/CEO Carlos Guestrin)

    Practical machine learning techniques for building intelligent applications (a previous episode of the Data Show featuring Mikio Braun, data scientist at Zalando)

    Stream processing and messaging systems for the IoT age (a previous episode of the Data Show featuring MC Srivas, co-founder of MapR)

    Machine Learning - an O’Reilly Learning Path

    —Huffduffed by mitchellkoch

  2. Spinning Plates and Creative Algorithms

    On episode eight we talk with Charles Sutton, a professor in the School of Informatics University of Edinburgh about computer programming and using machine learning how to better understand how it’s done well.

    Ryan introduces us to collaborative filtering, a process that helps to make predictions about taste. Netflix and Amazon use it to recommend movies and items. It’s the process that the Netflix Prize competition further helped to hone.

    Plus, we take a listener question on creativity in algorithms.

    —Huffduffed by mitchellkoch

  3. /Filmcast: The Wolf of Wall St.

    The /Filmcast: Bonus Ep. – The Wolf of Wall St. (GUEST: Jeff Cannata from Newest Latest Best)

    Posted on Sunday, January 5th, 2014 by David Chen

    Dave, Devindra and Jeff from Newest Latest Best discuss Martin Scorsese’s latest masterpiece, The Wolf of Wall St.. Be sure to check out more info on Jeff’s newest play. Also, see A.O. Scott’s great review of Wolf of Wall St. 

    You can always e-mail us at slashfilmcast(AT)gmail(DOT)com, or call and leave a voicemail at 781-583-1993. Also, like us on Facebook!Download or Play Now in your Browser:

    Subscribe to the /Filmcast:



    We’re Taking a Break! To tide you over…

    Subscribe to Dave’s Youtube videos

    Follow Devindra’s coverage of CES

    Check out Jeff’s play 

    Featured Reviews

    (9:00) Wolf of Wall St.

    (32:00) SPOILERS

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    Comment Now!

    —Huffduffed by mitchellkoch

  4. Podcast: Reviewing ‘The Wolf of Wall Street’, ‘Walter Mitty’, ‘Anchorman 2’ and More | Rope of Silicon

    —Huffduffed by mitchellkoch

  5. Captain America: Civil War Spoiler Special with Joe & Anthony Russo, Kevin Feige

    An epic, two-hour, spoilerific discussion of Marvel’s latest superhero dust-up, with Marvel boss Kevin Feige, and Civil War directing team Joe & Anthony Russo. CONTAINS SPOILERS.

    —Huffduffed by mitchellkoch

  6. ‘Captain America: Civil War’ Captures Politics Of The Moment : NPR

    The Marvel Cinematic Universe’s new movie, Captain America: Civil War, opens Friday. As a character, Captain America has long responded to the politics of the time and this movie is no different.

    —Huffduffed by mitchellkoch

  7. Movie Review: Captain America: Civil War

    It’s an Avengers movie! It’s a Captain America movie! It’s an Avengers movie AND a Captain America movie! But is it any good? Don’t forget to hit the "Subscr…

    Original video:
    Downloaded by

    —Huffduffed by mitchellkoch

  8. Joe and Anthony Russo on ‘Captain America: Civil War’

    Marvel’s newest blockbuster has so many super-characters, it’s no wonder it took two directors to handle all the action. Brothers Anthony and Joe Russo tell us how they went from directing quirky TV shows to big-budget superhero movies.


    Tagged with improvement

    —Huffduffed by mitchellkoch

  9. 160: Supine Podcasting with John Roderick

    John Roderick returns to Systematic to talk about the mental health of

    politicians, the importance of song lyrics, and outer space, among other


    —Huffduffed by mitchellkoch

  10. 160: Supine Podcasting with John Roderick

    John Roderick returns to Systematic to talk about the mental health of

    politicians, the importance of song lyrics, and outer space, among other


    —Huffduffed by mitchellkoch

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