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
Sarah Masud
@sara_02
Submitted Jun 7, 2017
Understanding the user’s intent can help the product team dramatically improve the user’s experience. Be it adding the right products to a shopping cart, stocks to the portfolio or packages to a software stack, the user’s intent drives the choices and products added. When designing recommendation systems, modelling intent is non-trivial. The intent behind the action is hidden. This talk is about how the speaker used probabilistic machine learning to model intent.
** Mad Hatter: Can you find a needle in the haystack?
Alice: Yup - if I know that it’s an iron needle. Give me the magnet! **
Consider the case of a new developer navigating the technology landscape to pick the libraries required to build her software application, or a new bride-to-be planning her bridal outfit on an e-commerce website.
What’s common? The dilemma - what’s the right set of choices that will click; the choices that will help them succeed in their intent.
In the world of Machine Learning, Recommendation Systems are widely used to solve the above problem. But the platform hosts a long tail to choose from. How could I make the recommendation system work?
By modelling intent first.
A two-stage model was built.
1. At the first stage, based on the user’s metadata, unsupervised clustering algorithm was employed to segment the users. This will help answer who the user is
2. For each user type, probabilistic machine learning models were used.
The talk discusses:
A real-time demo of the application that’s hosted on OpenShift will be showcased.
** Key Takeaways:**
Sarah is an engineer at Red Hat where she works on developer-oriented analytic projects. Her bachelor’s thesis on Topics Modeling was presented at Ninth International Conference on Contemporary Computing. She is currently a mentor with the Next Scholars Program and the Global Give Back Circle. With her mentorship work, she hopes to increase the participation of women in tech. She also volunteers her time with Women Who Code, Lean In India, and Systers. She is ever enthusiastic about Data Science, Women in STEM, and Open Source.
https://docs.google.com/presentation/d/1hP5RBuVLTtz_yGgKXX2jm_7Ly5e_0IKUrXE9T7WQdcc/edit?usp=sharing
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') }}