Jul 2018
23 Mon
24 Tue
25 Wed
26 Thu 07:45 AM – 06:15 PM IST
27 Fri 07:45 AM – 05:35 PM IST
28 Sat
29 Sun
Jul 2018
23 Mon
24 Tue
25 Wed
26 Thu 07:45 AM – 06:15 PM IST
27 Fri 07:45 AM – 05:35 PM IST
28 Sat
29 Sun
##About the conference and topics for submitting talks:
The Fifth Elephant is rated as India’s best data conference. It is a conference for practitioners, by practitioners. In 2018, The Fifth Elephant will complete its seventh edition.
The Fifth Elephant is an evolving community of stakeholders invested in data in India. Our goal is to strengthen and grow this community by presenting talks, panels and Off The Record (OTR) sessions that present real insights about:
**
##Target audience:
You should attend and speak at The Fifth Elephant if your work involves:
##Perks for submitting proposals:
Submitting a proposal, especially with our process, is hard work. We appreciate your effort.
We offer one conference ticket at discounted price to each proposer, and a t-shirt.
We only accept one speaker per talk. This is non-negotiable. Workshops may have more than one instructor.
In case of proposals where more than one person has been mentioned as collaborator, we offer the discounted ticket and t-shirt only to the person with who the editorial team corresponded directly during the evaluation process.
##Format:
The Fifth Elephant is a two-day conference with two tracks on each day. Track details will be announced with a draft schedule in February 2018.
We are accepting sessions with the following formats:
##Selection criteria:
The first filter for a proposal is whether the technology or solution you are referring to is open source or not. The following criteria apply for closed source talks:
The criteria for selecting proposals, in the order of importance, are:
No one submits the perfect proposal in the first instance. We therefore encourage you to:
Our editorial team helps potential speakers in honing their speaking skills, fine tuning and rehearsing content at least twice - before the main conference - and sharpening the focus of talks.
##How to submit a proposal (and increase your chances of getting selected):
The following guidelines will help you in submitting a proposal:
To summarize, we do not accept talks that gloss over details or try to deliver high-level knowledge without covering depth. Talks have to be backed with real insights and experiences for the content to be useful to participants.
##Passes and honorarium for speakers:
We pay an honorarium of Rs. 3,000 to each speaker and workshop instructor at the end of their talk/workshop. Confirmed speakers and instructors also get a pass to the conference and networking dinner. We do not provide free passes for speakers’ colleagues and spouses.
##Travel grants for outstation speakers:
Travel grants are available for international and domestic speakers. We evaluate each case on its merits, giving preference to women, people of non-binary gender, and Africans. If you require a grant, request it when you submit your proposal in the field where you add your location. The Fifth Elephant is funded through ticket purchases and sponsorships; travel grant budgets vary.
##Last date for submitting proposals is: 31 March 2018.
You must submit the following details along with your proposal, or within 10 days of submission:
##Contact details:
For more information about the conference, sponsorships, or any other information contact support@hasgeek.com or call 7676332020.
Hosted by
Somya Kumar
@somyak
Submitted Mar 26, 2018
There are various open source frameworks like Tensorflow, CNTK, MXNET, Pytorch etc which allow data scientists to develop deep learning models. Traditionally, data scientists train models on a single machine, however as datasets and models grow, model training on a single node becomes inefficient. There are a couple of frameworks like tensorflow which support model training on multiple machines using data and model parallelism. However, running these jobs across machines require configuring the jobs with cluster specific information explicitly which is cumbersome and time consuming for data scientists.
We (at Qubole) are trying to solve this problem by integrating different available solutions for distributed training in our clusters. There are interesting projects in the open source community like Horovod by Uber and TensorFlowOnSpark by Yahoo to make distribution across machines easier. Horovod brings HPC techniques to Deep Learning by using ring-all-reduce approach which speeds up model training. It also exposes APIs to convert a non distributed tensorflow code to a distributed one. TensorFlowOnSpark takes away all the complexities of specifying cluster spec by using Spark as the orchestration layer. In this talk, I will discuss various architectures followed by a comparative analysis and benchmarking of techniques available for distributed deep learning.
https://www.linkedin.com/in/somya-kumar-b4b62092
https://docs.google.com/presentation/d/14IV8Hmf8_loRk1ZxSKIhFIunI-7caCCWn46A2MjD7AQ/edit?usp=sharing
Jul 2018
23 Mon
24 Tue
25 Wed
26 Thu 07:45 AM – 06:15 PM IST
27 Fri 07:45 AM – 05:35 PM IST
28 Sat
29 Sun
Hosted by
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