##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.
Practical Recommendation Systems: Scalability, Accuracy, Latency
This tutorial shall cover traditional and modern recommendation systems from a perspective of practical application, in an easy question-answer format. The content of this tutorial is derived from multiple state-of-the-art research papers as well as classical text books on recommendation systems.
- Can a good recommendation system change the entire landscape of a market?
- Offline vs online
- Search vs discovery
- Ultimate user experience
- What are traditional recommendation systems and why they don’t work well in today’s world?
- Matrix factorization
- Content based recommendation
- Collaborative filtering based recommendation
- Why are the traditional recommendation systems not enough
- What are modern recommendation systems and what is the cost?
- Hybrid recommendation systems
- Machine Learning based recommendation systems
- Era of Deep Learning, Factorization Machines etc
- Customised evaluation techniques and loss function to achieve end goals better
- Conclusion: can we build a large scale awsome recommendation system like Youtube, Netflix or Spotify?
- What is these companies’ secret recipe behind their recommendation engine
- Latency, scalability, accuracy
- Infrastructure and model deployment cost
- traditional recommendation systems
- modern recomendation systems
- Scalability, Accuracy, Latency
- Cost benefit analysis
- Examples of state-of-the-art recommendation systems in the market
Nothing specific. People should have a basic understanding of Machine Learning.
Kunal Kishore completed his Bachelor of technology degree from IIT Kharagpur in Electronics and Communication Engineering. Currently he works as Research Scientist at Inmobi where he leads the data science efforts on Inmobi’s CDP offering. He has previously worked on data science areas such as large scale content recommendation systems, ad response prediction for display advertising bidder and e-commerce product recommendation.