mitchellkoch / Mitchell Koch

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Huffduffed (579)

  1. Tony Robbins - The Body You Deserve 01

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  2. Must Watch ! Best video of Tony Robbins The Power of Influence

    Must Watch ! Best video of Tony Robbins The Power of Influence

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  3. Not My Job: Tony Robbins Gets Quizzed On Laziness : NPR

    The life coach behind the Unleash the Power Within seminars has built his career around motivating people. We invited the go-getter to answer three questions about incredibly lazy people.


    Tagged with life coach

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  4. Tony Robbins: How To Have More Energy For Life

    CLICK HERE to Work with us ….

    Tony Robbins Energy For Life is probably training video we all should watch time to time. Come and watch it for FREE right here. It will remind you some important things in our lives we dont do very often anymore. However these things are super important. Its the whole reason of our existence. Enjoy.

    Tony Robbins Energy For Life (FREE Training session)

    Tony Robbins Energy For Life is very well know motivational training for todays people who seems to forgot why we live. I doesnt matter actually what is your problem. Maybe you are little lost in your business or just thinking of starting your business. Many people who are making money online in Internet marketing industry use Tony Robbins training.

    MORE at

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  5. Tony Robbins - How to Build Self Confidence | Tony Robbins Motivation

    Tony Robbins - How to Build Self Confidence | Tony Robbins Motivation ►CLICK HERE TO DOWNLOAD YOUR FREE BOOK :

    ►SEE MORE VIDEO HERE:Tony Robbins - How to Build Self Confidence | Tony Robbins Motivation : ►About Tony Robbins: Tony Robbins is is an American motivational speaker , Tony Robbins is an personal finance instructor, and Tony Robbins is self-help author. Tony Robbins became well known from Tony Robbins’s infomercials and self-help books: Unlimited Power, Unleash the Power Within and Awaken the Giant Within. In 2007, Tony Robbins was named in Forbes magazine’s "Celebrity 100" list. Forbes estimated that Tony Robbins earned approximately US$30 million in that year. ►Early life of Tony Robbins: Tony Robbins was born Anthony J. Mahavorick in North Hollywood, California, on February 29, 1960.Tony Robbins is the eldest of three children and his parents divorced when Tony Robbins was 7. Tony Robbins’s mother then had a series of husbands, including Jim Robbins, a former semi-professional baseball player who legally adopted Anthony when Tony Robbins was 12.

    His father couldn’t provide for their family so Tony Robbins left them. Tony Robbins’s mother started abusing alcohol and prescription drugs sometime after. While growing up, Tony Robbins was a primary care-giver, and helped provide for his siblings. Ton…

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  6. Part 2 here

    "Our revenues are now over $5 billion annually. Without access to Tony and his teachings, wouldn’t exist today." - Marc Benioff, Founder of Salesforce.

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  7. Part 1 here

    "Our revenues are now over $5 billion annually. Without access to Tony and his teachings, wouldn’t exist today." - Marc Benioff, Founder of Salesforce.

    —Huffduffed by mitchellkoch

  8. 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

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  9. 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

  10. /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.

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