サクサク読めて、アプリ限定の機能も多数！
トップへ戻る
ブラックフライデー
amplab.cs.berkeley.edu
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. However, widely-popular batch-processing computational frameworks like MapReduce and Spark were not designed to support the asynchronous and communication-intensive
Splash is a general framework for parallelizing stochastic learning algorithms (SGD, Gibbs sampling, etc.) on multi-node clusters. It consists of a programming interface and an execution engine. You can develop any sequential stochastic algorithm using the programming interface without considering the underlying distributed computing environment. The only requirement is that the base algorithm mus
I’ve found myself engaged with the Media recently, first in the context of a “Ask Me Anything” (AMA) with reddit.com http://www.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/ (a fun and engaging way to spend a morning), and then for an interview that has been published in the IEEE Spectrum. That latter process was disillusioning. Well, perhaps a better way to say it is that I
Back in June, Patrick Wendell posted a first set of results in a “Big Data Benchmark” for large-scale query engines. Obviously a lot has happened in the space since then and so we have updated those results, re-running the tests on the latest versions of the previously tested systems (Redshift, Impala, Spark, and Hive) and including numbers for the Tez (Stinger) system. While all the systems exa
Apache Spark and Shark have made data analytics faster to write and faster to run on clusters. This post will teach you how to use Docker to quickly and automatically install, configure and deploy Spark and Shark as well. How fast? When we timed it, we found it took about 42 seconds to start up a pre-configured cluster with several workers on a laptop. You can use our Docker images to create a loc
In addition to BDAS, the AMPLab has released additional software components useful for processing data: AMPCrowd: A RESTful web service for sending tasks to human workers on crowd platforms like Amazon’s Mechanical Turk. Used by the SampleClean project for context-heavy data cleaning tasks. Roadmap BDAS will continue to evolve over the life of the AMPLab project, as existing components evolve and
Click Here for the previous version of the benchmark Introduction Several analytic frameworks have been announced in the last year. Among them are inexpensive data-warehousing solutions based on traditional Massively Parallel Processor (MPP) architectures (Redshift), systems which impose MPP-like execution engines on top of Hadoop (Impala, HAWQ), and systems which optimize MapReduce to improve per
Data Drives Decisions Today, more and more organizations collect more and more data, and they do so with one goal in mind: extracting value. In most cases, this value comes in the form of decisions. There are myriad examples of data driving decisions: (1) monitoring network traffic to detect and defend against a cyber attack, (2) using clinical and genomic data to provide personalised medical trea
Turning up the volume on big data Scale, immediacy, & continuous improvement The datacenter as a computer Leveraging human intelligence and activity Working at the intersection of three massive trends: powerful machine learning, cloud computing, and crowdsourcing, the AMPLab is creating a new Big Data analytics platform that combines Algorithms, Machines and People to make sense at scale. Machine
このページを最初にブックマークしてみませんか？
『AMPLab - UC Berkeley』の新着エントリーを見る
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
