Anthill Inside 2017
On theory and concepts in Machine Learning, Deep Learning and Artificial Intelligence. Formerly Deep Learning Conf.
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu
28 Fri
29 Sat 09:00 AM – 05:40 PM IST
30 Sun
##About AnthillInside:
In 2016, The Fifth Elephant branched into a separate conference on Deep Learning. Anthill Inside is the new avataar of the Deep Learning conference.
Anthill Inside attempts to bridge the gap bringing theoretical advances closer to functioning reality.
Proposals are invited for full length talks, crisp talks and poster/demo sessions in the area of ML+DL. The talks need to focus on the techniques used, and may be presented independent of the domain wherein they are applied.
We also invite talks on novel applications of ML+DL, and methods of realising the same in hardware/software.
Case studies of how DL and ML have been applied in different domains will continue to be discussed at The Fifth Elephant.
https://anthillinside.in/2017/
##Format:
Anthill Inside is a two-track conference:
We are inviting proposals for:
You must submit the following details along with your proposal, or within 10 days of submission:
##Selection Process:
We expect you to submit an outline of your proposed talk, either in the form of a mind map or a text document or draft slides within two weeks of submitting your proposal to start evaluating your proposal.
You can check back on this page for the status of your proposal. We will notify you if we either move your proposal to the next round or if we reject it. 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.
A speaker is NOT confirmed a slot unless we explicitly mention so in an email or over any other medium of communication.
There is only one speaker per session. Entry is free for selected speakers.
We might contact you to ask if you’d like to repost your content on the official conference blog.
##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.
Please note, we will not evaluate proposals that do not have a slide deck and a video in them.
Hosted by
Rajesh Gudikoti
@ragudiko
Submitted Jun 7, 2017
We can build the machine learning model which can understand the linguistic nuances and relationships specific to a industry. Once model is trained and evaluated, you can use it to extract domain specific entities from new documents.
Current natural language processing techniques cannot extract/interpret the data as required by domain/industry specific. The data(entities) represent different meaning in different domain. To overcome such business issue we have tool by which you can Seamlessly create and deploy industry specific models for building cognitive apps.
In this tool we build type system specific to industry. The type system consists of entities and relationship between entities. For e.g.
In case of Employee and Employeer, employee and employer are entities and employedBy can represnted as relationship between two entities.
SMEs of the industry have better understanding of business, so their inputs will be required to build type system.
Human Annotators guide the system to understand the semantics of the industry by annotating(mapping) the text/phrase with specific entities.
Based on human annotation the machine learning model is trained and evaluated.
The tool provides option to anlayze the performance of the model. If you are satisfied with model, you can go ahead and use to anlayze new sms text. The performance of the system are represented by scores which helps us understand how many and which entities were correctly identified.
Basic understanding of Natural Language Processing.
Rajesh is working as Architect at IBM, India Digital Business Group. I have been part of product development in various domains.
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