How to Make Synthetic Data | Synthetic Data Generation for Machine Learning

The video tutorial covers synthetic data generation, its benefits, and challenges. It explains how synthetic data can expand training datasets and improve model robustness. The tutorial demonstrates using Sklearn for simple data generation, data augmentation with Fashion-MNIST, and explores generative adversarial networks (GANs) for creating synthetic MNIST digits. It also highlights the use of Weights and Biases for data visualization and tracking metrics.

What is synthetic data and how is it generated?

Why might synthetic data be useful for machine learning models?

What are some advantages of using synthetic data over real data?

In what ways can synthetic data help in medical diagnostics?

Can you explain the limitations of using synthetic data?

Describe the example of OpenAI's project with the Rubik's cube and its relevance to synthetic data.

What are some methods for generating synthetic data using Sklearn?

What is the Fashion-MNIST data set and how is it used in the context of synthetic data?

How does data augmentation relate to synthetic data generation?

What challenges might arise when transitioning from synthetic data to real-world applications?