Noise-Injection-Techniques. Noise Injection Techniques provides a comprehensive exploration of methods to make machine learning models more robust to real-world bad data. This repository explains and demonstrates Gaussian noise, dropout, mixup, masking, adversarial noise, and label smoothing, with intuitive explanations, theory, and practical code examples.

Problem

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Audience

The full read on this project is a sign-in away — what it solves, who it serves, and where it sits in the market.

Market

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Pricing

The full read on this project is a sign-in away — what it solves, who it serves, and where it sits in the market.