Synthetic Data Generation using Autoencoders: A Powerful Approach for Fake but Realistic Data
As data scientists, we often face the challenge of collecting and labeling large amounts of data, especially in scenarios where data is scarce or expensive to obtain. One innovative solution to this problem is synthetic data generation, which involves creating fake data that mimics the characteristics of real data. In this post, we'll explore how autoencoders can be used to generate synthetic customer reviews, and provide a code snippet to get you started.
What are Autoencoders?
Autoencoders are a type of neural network that consists of an encoder and a decoder. The encoder compresses the input data into a lower-dimensional representation, while the decoder reconstructs the original data from this compressed representation. In the context of synthetic data generation, autoencoders can be trained to learn the underlying patterns and structures of the real data, and then used to ge...
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