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.
Anthill Inside 2019 sponsors:
Portfolio Optimization using Deep Reinforcement Learning
What is Portfolio Management? What is Deep Learning? How does one apply deep learning to the complex problem of portfolio management? What is the intuitive interpretation of this application? What lies under the black box?
In this talk the speaker would answer these questions and demonstrate the usage of a deep machine learning model for portfolio management of a basket of cryptocurrencies using reinforcement learning. She will also present backtested results of how this advanced approach compare to the traditional approaches and what are reasons for the outperformance.
The speaker would start of by introducing the problem of portfolio management followed by explaining deep reinforcement learning.
She would then introduce the problem at hand - applying deep RL for Portfolio Management on a basket of cryptocurrencies and the rationale behind choosing this particular problem.
She will show what lies under the hood in this problem by explaining the framework and the methodology.
She will end by presenting the results and their interpretation.
Sonam Srivastava is a Senior Quantitative Researcher at qplum, an Investment Management startup, focussed on the application of data science in Systematic Investment. She was previously a Senior Associate at HSBC Global Banking and Markets. She has more than 7 years of experience working in Algorithmic Trading, Systematic Risk Trading and Equity Index Structuring. She has worked at the forefront of advanced algorithmic applications in Trading and has a deep belief that advanced machine learning when used correctly can provide a great edge in the Financial Markets.
She holds a Masters in Financial Engineering from Worldquant University and an Bachelors in Technology from IIT Kanpur.