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Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms

Stochastic Approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. The most famous examples today are TD- and Q-learning algorithms. This three hour tutorial lecture series, courtesy of the Simon Institute for the Theory of Computing at UC Berkeley, consists of two video segments.

GTC Interview: AI Ready Solutions from Dell EMC

In this video from the GPU Technology Conference, Kash Shaikh from Dell EMC describes the company’s new AI Ready Solutions. “Dell EMC is at the forefront of AI, providing the technology that makes tomorrow possible, today. Dell EMC uniquely provides an extensive portfolio of technologies — spanning workstations, servers, networking, storage, software and services — to create the high-performance computing and data analytics solutions that underpin successful AI, machine and deep learning implementations.”

The Future of Cognitive Computing

In the video presentation below, Dr. John Kelly III, Senior Vice President, IBM Research and Solutions Portfolio, discusses the future of cognitive computing. Dr. Kelly is focused on the company’s investments in several of the fastest-growing and most strategic parts of the information technology market, including IBM Watson.

Big Data Meets HPC – Exploiting HPC Technologies for Accelerating Big Data Processing

DK Panda from Ohio State University gave this talk at the Stanford HPC Conference. “This talk will provide an overview of challenges in accelerating Hadoop, Spark and Memcached on modern HPC clusters. An overview of RDMA-based designs for Hadoop (HDFS, MapReduce, RPC and HBase), Spark, Memcached, Swift, and Kafka using native RDMA support for InfiniBand and RoCE will be presented.”

Analytics Development Life Cycle: Pangea is Panacea

Sai Prakash from HCL America gave this talk at the Stanford HPC Conference. “In this short talk we shall present an analytics workbench perspective (Pangea) that brings entire ADLC under single umbrella thus enabling collaboration, shrinking overall cycle time, easing model deployment efforts and allowing model monitoring. Actionable insights and visualizations are facilitated though service integration interfaces.”

Slidecast: Striim – Streaming Integration with Intelligence

Coming to you from our popular “Rich Report Podcast” channel, we offer a new slidecast with Steve Wilkes, Co-founder and CTO at Striim presenting: Striim – Streaming Integration with Intelligence. Striim recently announced the launch of version 3.8 of the Striim platform, with 47 new and enhanced capabilities.

Quadcopter Navigation in the Forest using Deep Neural Networks

In the video presentation below, a group of deep learning researchers study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image saliency or appearance contrast; the team proposes a different approach based on a Deep Neural Network used as a supervised image classifier.

Top 5 Mistakes When Writing Spark Applications

In the presentation below from Spark Summit 2016, Mark Grover goes over the top 5 things that he’s seen in the field that prevent people from getting the most out of their Spark clusters. When some of these issues are addressed, it is not uncommon to see the same job running 10x or 100x faster with the same clusters, the same data, just a different approach.

An Introduction to NoSQL Databases

In the video presentation below, Bart Baesens, Professor of Big Data & Analytics, discusses a series of non-relational database management systems which focus specifically on being highly scalable in a distributed environment: NoSQL databases. The presentation is an extract from an upcoming book Principles of Database Management.

How Computers Learn

This Vienna Gödel Lecture provides a fascinating talk by Peter Norvig, Research Director at Google Inc. in the field of intelligent computers. Norvig talks about his long experience in AI and Machine Learning. The talk explains how computers learn from examples and what are the promises and limitations of these techniques.