Conferences / PyData
PyData Delhi 2019
Aug 3–4, 2019 · IIIT-Delhi (Indraprastha Institute of Information Technology Delhi), New Delhi, India
- Conference
- PyData Data science & ML
- Dates
- Aug 3, 2019 – Aug 4, 2019
- Venue
- IIIT-Delhi (Indraprastha Institute of Information Technology Delhi)
- Place
- New Delhi, India
- Format
- In person
- Status
- Past
- Attendance
- —Homepage: "Over 400 participants are waiting for you!" (pre-event claim, not a reported headcount).
- Website
- pydata.org
- Recorded
- 11 sponsors · 33 speaker appearances
- Notes
- Branded "PyData Delhi 2019". Speakers: all sessions on the conference schedule (affiliations parsed from speaker-profile bios) plus the two keynotes listed on the homepage (Viral B. Shah, Julia Computing; Prukalpa Sankar, Atlan & SocialCops). Ticket prices not found.
Ticket prices
No ticket prices recorded
Sponsors 11
Speakers and talks 33
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Prukalpa Sankar | Atlan | Keynote | — | — |
| Viral B. Shah | Julia Computing | Keynote | — | — |
| Aanish Singla | — | Talk | Automated Large Scale Forecasting for 1000+ products | — |
| Abhishek Mungoli | — | Talk | How GPU Computing literally saved me at work | — |
| Ankit Rathi | SITA | Talk | Understanding Opacity in Machine Learning Models | — |
| Anubav Kesari | Exzeo | Talk | Advance ML- On Improving Performance of Machine Learning Models by Optimizing Hyper-parameters | — |
| Arjun Bahuguna | — | Talk | Machine Learning Security - The Data Scientist's Guide to Hardening ML Models | — |
| Bhargav Srinivasa Desikan | University of Washington | Talk | Tips, Tricks and Topics in Text Analysis | — |
| Deepak Sharma | Clustr | Talk | Deep Sequence Models for Attribute Extraction from Product Titles | — |
| Deya Chatterjee | ISI Kolkata | Talk | Building robust AI models against adversarial attacks: Python libraries Cleverhans and Foolbox | — |
| Harshit Joshi | — | Talk | Quantitative Finance with R | — |
| Jaidev Deshpande | Gramener | Talk | Fundamental Results in ML and How to Use Them | — |
| Janu Verma | Hike | Talk | Transfer Learning in Natural Language Processing | — |
| Karthik Vijayakumar | GoPay | Sponsor | Understanding user churn in GoPay (Sponsored Talk) | — |
| Kaustubh Hiware | Mercari | Talk | Using NLP for disaster management | — |
| Keshav Joshi | GeorgiaTech | Talk | Generating tabla note sequences with Markov Chains and Processing/p5.js | — |
| Mayank Jain | Barclays | Sponsor | Predicting Real-Time Transaction Fraud Using Python and spark (Sponsored Talk) | — |
| Mridu Bhatnagar | Goibibo.com | Talk | Automating Data Pipeline using Apache Airflow | — |
| Nikita Sharma | Nagarro | Talk | Automated Large Scale Forecasting for 1000+ products | — |
| Pankaj Gupta | Barclays | Talk | Improving Model Lifecycle Management for Machine Learning Models | — |
| Parth Raghav | Reed College | Talk | Generating Sorting Networks using NEAT | — |
| Pranav Suri | WorldQuant | Talk | How I Used Data Visualization in My Quest for the Perfect Wine | — |
| Rajesh Golani | Stanford University | Talk | Building interactive data visualisations with Altair & Django | — |
| Sarah Masud | — | Talk | The Magic of Numpy | — |
| Sayantan Gangopadhyay | Microsoft | Talk | Autocorrect of words and Autosuggest of Sentences | — |
| Sleeba Paul | Perleybrook Labs | Talk | Auria Kathi - The power of Multi Model Machine Learning Pipelines | — |
| Sonal Kukreja | — | Talk | Image Manipulation Detection using Neural Networks | — |
| Sruthi S | GoPay | Sponsor | Understanding user churn in GoPay (Sponsored Talk) | — |
| Suyog Swami | dunnhumby | Talk | Knowledge Graph made simple using NLP and Transfer Learning | — |
| Tanay Agrawal | — | Talk | Advance ML- On Improving Performance of Machine Learning Models by Optimizing Hyper-parameters | — |
| Vedant Bonde | — | Talk | Quantitative Finance with R | — |
| Victor Robin | dunnhumby | Talk | Intelligent recruitment using NLP and machine learning to identify the most suitable candidates | — |
| Yatin Bhatia | — | Talk | Understanding Opacity in Machine Learning Models | — |