From commits-return-1207-apmail-community-commits-archive=community.apache.org@community.apache.org Sat Mar 7 18:06:39 2015 Return-Path: X-Original-To: apmail-community-commits-archive@minotaur.apache.org Delivered-To: apmail-community-commits-archive@minotaur.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 3348B10A34 for ; Sat, 7 Mar 2015 18:06:39 +0000 (UTC) Received: (qmail 84588 invoked by uid 500); 7 Mar 2015 18:06:39 -0000 Delivered-To: apmail-community-commits-archive@community.apache.org Received: (qmail 84559 invoked by uid 500); 7 Mar 2015 18:06:39 -0000 Mailing-List: contact commits-help@community.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: dev@community.apache.org Delivered-To: mailing list commits@community.apache.org Received: (qmail 84549 invoked by uid 99); 7 Mar 2015 18:06:39 -0000 Received: from arcas.apache.org (HELO arcas.apache.org) (140.211.11.28) by apache.org (qpsmtpd/0.29) with ESMTP; Sat, 07 Mar 2015 18:06:39 +0000 Date: Sat, 7 Mar 2015 18:06:39 +0000 (UTC) From: "Lee moon soo (JIRA)" To: commits@community.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Commented] (COMDEV-118) Zeppelin GSoC Project: create a ML\Deeplearning tutorial notebook MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit X-JIRA-FingerPrint: 30527f35849b9dde25b450d4833f0394 [ https://issues.apache.org/jira/browse/COMDEV-118?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14351721#comment-14351721 ] Lee moon soo commented on COMDEV-118: ------------------------------------- Hi Zezhong ZHANG, Really appreciate for the interest. I think data that we want to use is any interesting data, which is licensed to use in this kind of Purpose. About "interactive machine learning", i think the subject is wide open. >From integration with existing machine learning software and giving them ability to analysis, (Recently published integration with PredictionIO http://docs.prediction.io/datacollection/analytics-zeppelin/), Some utility library or visualization that helps machine learning can be used in interactive notebook in Zeppelin, to any new great idea. Welcome! > Zeppelin GSoC Project: create a ML\Deeplearning tutorial notebook > ----------------------------------------------------------------- > > Key: COMDEV-118 > URL: https://issues.apache.org/jira/browse/COMDEV-118 > Project: Community Development > Issue Type: Task > Reporter: Alexander Bezzubov > Labels: deeplearning, gsoc2015, machine_learning, scala, spark > > Zeppelin as generic notebook interpreter system for cluster computing is capable of connecting different distributed systems. The main focus right now is Apache Spark which itself have extensive machine learning facilities. > There are other systems such as H2O, PredictIO, Parameterserver or Singa implementing advanced techniques based on neural networks, commonly referred as Deeplearning. > The goal of this Google Summer of Code project is to develop a tutorial Notebook with a sample data and extend Zeppelin server to be able to show advantages of interactive machine learning. > Stunents are welcome to reach out Zeppelin community via its developers mailing list to discuss the details, see http://zeppelin.incubator.apache.org/community.html -- This message was sent by Atlassian JIRA (v6.3.4#6332)