##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.
We see quite a lot of excitement around Reinforcement Learning (RL) in industry and academia. We regularly see new industrial applications both inside and outside of Amazon. We also see RL papers dominating many ML conferences such as NeurIPS and ICML. RL is promising for multiple reasons that include a better formulation of the problem which allows for optimization of multi-step decision making process and its ability to learn strategies for unseen, dynamic scenarios. RL has gained traction in recent years with the use of deep neural networks to accomplish astonishing results in applications such as games, robotics, recommendations and operations research problems. We propose this workshop to bring people together to share ideas, increase awareness and build a community around RL.
The workshop is designed to give an overview of RL and dive deep into some of the state-of-the-art techniques in RL.
- Definitions and Problem Statement
- Classical Approaches
- Evolutionary Algorithms
- Temporal Differences
- Exploration and Exploitation
- Evaluating Reinforcement Learning Algorithms
- On-Policy Learning
- Assigning Credit and Blame to Paths
- Model-Based Methods
- Reinforcement Learning with Features
- Deep Reinforcement Learning
- Atari Games, Self-Driving and other applications
Laptop with Python installed/Cloud host
Suraj is a Senior Machine Learning Scientist working on a variety of Machine Learning problems at Amazon. Prior to this, he has worked at IBM Research(IRL) and Adobe. His areas of work include Natural Language Processing, Deep Recurrent Networks, CNNs for text, Reinforcement Learning, and Generative Models. Suraj has more than 8 years of industry experience in the field of Machine Learning, NLP, Deep Learning and Reinforcement Learning. He has presented papers/posters in AMLC for last three years, delivered talks/tutorials at GHCI, AITC and others. He has three USPTO approved patents. He has conducted Reinforcement Learning workshops at several occasions including Amazon SMML Workshop 2018. He is organizing “Reinforcement Learning” workshop at Amazon Machine Learning Conference 2019.