The Fifth Elephant 2017
On data engineering and application of ML in diverse domains
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
On data engineering and application of ML in diverse domains
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
##Theme and format
The Fifth Elephant 2017 is a four-track conference on:
The Fifth Elephant is a conference for practitioners, by practitioners.
Talk submissions are now closed.
You must submit the following details along with your proposal, or within 10 days of submission:
##About the conference
This year is the sixth edition of The Fifth Elephant. The conference is a renowned gathering of data scientists, programmers, analysts, researchers, and technologists working in the areas of data mining, analytics, machine learning and deep learning from different domains.
We invite proposals for the following sessions, with a clear focus on the big picture and insights that participants can apply in their work:
##Selection Process
We will notify you if we move your proposal to the next round or reject it. A speaker is NOT confirmed for a slot unless we explicitly mention so in an email or over any other medium of communication.
Selected speakers must participate in one or two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.
There is only one speaker per session. Entry is free for selected speakers.
##Travel grants
Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.
##Commitment to Open Source
We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.
##Important Dates:
##Contact
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.
Hosted by
Nabarun Pal
@palnabarun
Submitted Jun 10, 2017
Camera based intelligent applications are lot of fun! There are many practical applications of it like Industrial Counters, Real Time Object Tracking, Object Classification, Road Traffic Estimation etc. While they are fun and interesting, building them is not that trivial. Generally, building camera based intelligent applications require many modules in the pipelines and a data scientist may not be aware of those. It involves managing hardware, machine learning, a dashboard for user interaction and data visualization and most importantly a way to glue them all together. Taking care of all these requires a considerable amount of time to deploy an application.
I have been working on trying to build tools to simplify and streamline this process and allow data scientists to build such an application in days instead of weeks. In this talk, I will discuss about the approach we have taken, the tools that we have built, of which some are open source, and explain how these tools improved the overall time needed to build camera based intelligent applications.
The talk will be structured into 3 parts - 8-10mins for the presentation, 3-5mins for the hands-on demo to building an application from scratch and the rest for questions.
The presentation is structured as follows with details in slide link provided:
In the hands on demo part, I will go through building one image/video based machine learning problem that a data scientist may want to make using our open source modules. I will show how to deploy the application on the rorocloud platform or on your own servers or local machine.
Basic knowledge of building blocks of a machine learning application. None in terms of hardware.
The speaker is Nabarun Pal, an undergraduate student at Indian Institute of Technology Roorkee who just finished his pre-final year. Currently, he is working for rorodata which aims at providing data scientists a platform to build and deploy their models without the need of worrying about infrastructure, scalability and performance.
He is passionate about software development. He can also talk about Internet of Things, Electronics, Robotics with equal spirit. His journey with the field of software and robotics started in his schooling days. He represents the college in various Robotics competitions and was involved in projects related to the above domains, brief of which can be found here
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
Hosted by
{{ gettext('Login to leave a comment') }}
{{ gettext('Post a comment…') }}{{ errorMsg }}
{{ gettext('No comments posted yet') }}