##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to email@example.com
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Generation of Newsletter using Natural Language Generation
We will generate event narratives using NLG.
Assume I am part of event (like Anthill Inside). I conducted a session on particular topic (NLG). I make entry into internal tool. Below information are captured in database.
name of the event
type of event
location where event is held
number of people attended the event
When publishing team asks me to provide narrative of the event/my session, I start creating narratives by typing.
We conducted a session on “July 24, 2019” in “Bangalore”. “Rajesh Gudikoti” handled session on “Natural Language Generation”. The number of people attended was “80”. The event was live streamed to 2000 people. Some people showed enthusiasm to know more about implementation and different use cases of NLG.
How about generating event narrative automatically picking the data from database?
- What is NLG?
- What are best use cases for NLG
- Brief on sample implementation
I have spent 2 decades in software industry and worked on both applications and products. My experience is in Java, J2EE, BPM AI(Artificial Intelligence) and Data Science. Currently helping startups and enterprise clients to adopt IBM Watson platform. Involved in developing POCs on AI-NLP for clients. I handle technical seminars and workshops on AI, Kubernetes and Data Science. I am also fortunate to be part of panel discussions in IT as well as in Engineering College.
- ibm.biz/BdZtFt --> ML in Procurement - Assist Procurement Expert
- http://bit.ly/2x23ye3 --> Supervised Machine Learning without code