Please use this identifier to cite or link to this item:
https://ir.iimcal.ac.in:8443/jspui/handle/123456789/4874| Title: | Synthetic Data Generation: robust modelling with limited data |
| Authors: | Pushpam, Parijat |
| Keywords: | Synthetic data Amazon Fraud/Finance Domain Randomization Business implications AWS services General Data Protection Regulation Machine learning E-voting Healthcare data sharing Financial transactions Homomorphic encryption |
| Issue Date: | 2023 |
| Publisher: | Students of PGDBA Post Graduate Diploma in Business Analytics, IIM Calcutta |
| Series/Report no.: | Vol.4; |
| Abstract: | At the heart of every Machine Learning model lies the fundamental concept of “learning from data.” This singular essence sets Machine Learning apart from conventional programming and ignites our fascination with its potential to revolutionize industries and shape the future. The question is, how do we carry this out? Before answering this question, we should look at a famous scheme or flow of things that are, available in the data science literature everywhere. |
| URI: | https://ir.iimcal.ac.in:8443/jspui/handle/123456789/4874 |
| Appears in Collections: | AINA 4.0 - Volume 4 Edition 2022-23 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Synthetic Data Generation.pdf | Synthetic Data Generation | 1.47 MB | Adobe PDF | View/Open |
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