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.
Anthill Inside 2019 sponsors:
Time Series Anomaly detection on structured data from IOT Network using CNN
Time series anomaly detection and classification problems have existed and there are various existing solutions to tackle such kind of problems. However, all/most of the solutions are for ideal cases (having enough labelled data or developing a model with average precision/recall) and does not take into account the practical constraints of implementing and deploying a highly generalizable solution. <br />
In this talk, I intend to cover brief overview of how time series anomaly detection problems can be tackled with an unconventional approach which captures the multi-spatial relationships between time and various features, augment the dataset using deep generative models (GANs) and train a classifier to achieve state of the art results. I also intend to briefly touch upon an evaluation framework for measuring GAN performance by evaluating on explicitly parameterized, synthetic data distributions which can be applied to any dataset.
- Approaches that did not work relative to our benchmark of precision/recall
- Data transformation - an innovative way to transform time series data so as to capture the multi-spatial relationships between time and features.
- Building a deep generative model – configuration parameters for the model which worked in practical implementation and are reproducible
- Discuss the evaluation framework
- How to make this approach work in other domains
- Intermediate understanding of CNN
- Basic understanding of the nature of IoT data
Prasenjeet is a Senior Data Scientist working in Ericsson Global AI Accelerator(GAIA) Team. He has 10 years of industry experience in applying data science and advanced machine learning techniques to diverse sectors including telecom, finance, manufacturing and equity research for quantitative hedge funds. Married to Data Science (but dating Data Engineering). He is also helping a Silicon Valley start-up in finding its feet in the industry. He has keen interest in Open Source and its contributors and trying to be one of them. Has contributed to Scikit-learn and other python libraries.