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
krupal Modi
@superkrups
Submitted Jun 9, 2017
Evolution of automated messaging, which started in 1966 with first Chatbot, ELIZA, has now reached a stage where Chatbots have found there application in several industry domains like personal assistance, customer care, banking, e-commerce, healthcare, etc. With early experiments showing positive results , we have reached a stage where chatbots are no longer merely an application to play around with but have proven their utility in solving real problems. As a result, data scientists need to now figure out how to fuse NLP, conventional machine learning algorithms and deep learning systems into a single dialogue system which can scale easily across datasets from different domains and is capable of digesting training data from real conversations.
During our journey at Haptik, we ended up building and customizing different machine learning modules specifically focused on building Chatbots on narrow domains and targeted at end to end completion of a specific task such as making travel bookings, gift recommendation and ordering, lead generation for different businesses, etc. I would specifically like to share how our machine learning stack grew organically and finally found a stable state containing an ideal mix of simple and complex machine learning algorithms.
Highlighting different problems that chatbots are solving today with few examples. Introducing why dialogue systems needs to scale and efficiently utilize reseach that happened over last 5 decades.
How to build a simple system from ground zero which is good enough to go live and helps you collect next million messages.
Cluster your data and extend your system to use retrieval/classification algorithm to make a bit more intelligent.
Use complex deep learning models with simpler approaches and utilize every bit of conversational data available with you in most efficient way.
Make sure simpler conversations are catered by simple algorithms and complex ones are in your control while your Chatbot responds fast and accurate.
Open challenges existing in the industry and how to foresee/avoid them.
Basic understading of what are Chatbots and what is Machine Learning
https://in.linkedin.com/in/krupal-modi-a5946b32
I have worked as a Researcher, Engineer and Machine Learning Scientist during different stages of my career. I love to invent, patent, build and architect end to end machine learning solutions to make our life easy. One of my achievements includes creating a chatbot which has seen more than 200 million messages from different domains and is still learning with a long way to go forward. I love to share my learnings with the community by open sourcing and actively participating in Data Science meetups.
https://docs.google.com/presentation/d/1dzH0tCOOzkc9Et-YB3HVRcimiW8xY6VCQQ5k5xbvz9A/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
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