Dive deep into this tutorial on how to construct a machine learning model that effectively differentiates between fake and real data records. Whether you're trying to assess the quality of synthetic data or discern hybrid datasets, this guide will prove invaluable.
Here is what you'll learn:
00:00-00:02 - Fake vs Real Data Discriminator
00:05-00:20 - Purpose, Applications, and Evaluating Synthetic Data Quality
00:26-00:38 - Criteria for Realism in Synthetic Data
00:45-01:00 - Merging Real & Synthetic Data
01:11-01:18 - Impact of Limited Training Samples & MOSTLY AI's High-Quality Default Dataset
01:30-01:45 - Data Generation with MOSTLY AI
01:58-02:14 - Intentional Low-Quality to Concurrent High-Quality Synthetic Data Generation
02:37-03:00 - Job Completion, Downloads, and Uploading Data to Google Colab
03:06-03:53 - Starting with Low-Quality Dataset to Evaluation on the Holdout Dataset
04:02-05:10 - Dataset Overview, Discriminator's Performance, AUC Interpretation
Replicate the experiment using your data of choice, using MOSTLY AI's state-of-the-art synthetic data generation platform: https://bit.ly/43IGYSv