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Personalization and Scalable Deep Learning with MXNET

The presentation below by Alex Smola is “Personalization and Scalable Deep Learning with MXNET” from the MLconf San Francisco, 2016. User return times and movie preferences are inherently time dependent. In this talk, Alex shows how this can be accomplished efficiently using deep learning by employing an LSTM (Long Short Term Model). Moreover, he shows how to train large scale distributed parallel models using MXNet efficiently.

Monte Carlo Simulations in Ad-Lift Measurement Using Spark

In this talk from Spark Summit East 2016, Prasad Chalasani explores some of the challenges that arise in setting up scalable simulations in a specific application, and share some solutions and lessons learned along the way, in the realms of mathematics and programming.

Data Overload: Raising the Information Generation

In this TEDx presentatin, Reuben Vendeventer’s talk is pitched towards students, but it’s useful for business people too. The value in Vendeventer’s talk is his orientation towards data and the information age. He conceptualizes our knowledge of DNA as data points. This kind of reorientation is a solid start for any learner’s relationship with the data behind business intelligence.

The Weight of Data

In this TEDxVancouver presentation, Jer Thorp’s discussion is stellar because it frames the story of how data can tell our stories. His project, openpaths.cc, tracked (willing) participants’ iPhone location data, his own included. His take on his own data’s story—matching each location with his career journey.

FPGAs Speed Machine Learning at SC16 Intel Discovery Zone

In this video from SC16, Intel demonstrates how Altera FPGAs can accelerate Machine Learning applications with greater power efficiency. “The demo was put together using OpenCL design tools and then compiled to FPGA. From an end-user perspective, they tied it together using Intel MKL-DNN with CAFFE on top of that. This week, Intel announced the DLIA Deep Learning Inference Accelerator that brings the whole solution together in a box.”

The Math Behind Basketball’s Wildest Moves

If basketball is of more interest to you than business intelligence, you’ll like this TED talk by Rajiv Maheswaran. If you want pithy examples of what algorithms and machine learning are, you’ll like Maheswaran’s talk even more. Algorithms are necessary to the functioning of any BI software, and machine learning has been called “the new BI.” Googling those terms is useful, but a little dull.

It’s Time to Redesign Medical Data

In the TEDMED talk below, Thomas Goetz looks at medical data, making a bold call to redesign it and get more insight from it. Your medical chart: it’s hard to access, impossible to read — and full of information that could make you healthier if you just knew how to use it.

Data Analytics, Machine Learning, and HPC in Today’s Changing Application Environment

In this video from the Intel HPC Developer Conference, Franz Kiraly from Imperial College London and the Alan Turing Institute describes why many companies and organizations are beginning to scope their potential for applying rigorous quantitative methodology and machine learning.

Performance Optimization of Deep Learning Frameworks on Modern Intel Architectures

In this video from the Intel HPC Developer Conference, Elmoustapha Ould-ahmed-vall from Intel describes how the company is doubling down to optimize Machine Learning frameworks for Intel Platforms. Using open source frameworks as a starting point, surprising speedups are possible using Intel technologies.

Making Data Mean More Through Storytelling

In this TEDx talk, Ben Wellington discusses how to use data to tell stories. In fact, he draws on some key lessons from fields well outside computer science and data analysis to make his observations about New York City fascinating.