##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 firstname.lastname@example.org
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Recent advances in machine learning have rekindled the quest to build machines that can interact with outside environment like we human do - using visual clues, voice and text. An important piece of this trilogy are systems that can process and understand text in order to automate various workflows such as chat bots, named entity recognition, machine translation, information extraction, summarization, FAQ system, etc.
A key step towards achieving any of the above task is - using the right set of techniques to represent text in a form that machine can understand easily.
This would be a 2-day instructor-led hands-on training session to learn and implement an end-to-end deep learning model for natural language processing.
Unlike images, where directly using the intensity of pixels is a natural way to represent the image; in case of text there is no such natural representation. No matter how good is your ML algorithm, it can do only so much unless there is a richer way to represent underlying text data. Thus, whatever NLP application you are building, it’s imperative to find a good representation for your text data.
In this bootcamp, we will understand key concepts, maths, and code behind the state-of-the-art techniques for text representation. We will cover mathematical explanations as well as implementation details of these techniques. This bootcamp aims to demystify, both - Theory (key concepts, maths) and Practice (code) that goes into building these techniques. At the end of this bootcamp participants would have gained a fundamental understanding of these schemes with an ability to implement them on datasets of their interest.
Laptop and Lots of enthusiasm
I am part of Intuit AI team. Prior to this, I was heading ML efforts for Huawei Technologies, Freshworks, Chennai and Airwoot, Delhi. I dropped out of my Phd from IIT Delhi to work with startups. Before this I did my masters in theoretical computer science from IIIT Hyderabad and
I am a regular speaker at ML conferences like ODSC, Pydata, Nvidia forums, Fifth Elephant, Anthill. I have also conducted workshops, mostly attended by machine learning practitioners. I am also the co-organizer for one of the early Deep Learning meetup group in Bangalore. I was Editor of “Anthill-2018”.
- Sarcasm detection, ODSC 2018 . https://www.youtube.com/watch?v=FoR_-ELAcfE
- Synthetic Gradients, AntHill 2017 . https://www.youtube.com/watch?v=toZprSCCmNI