Effective Continuous Training Strategies using Real Negative Data

Effective continuous training strategies are crucial for the success of any machine learning model. One important aspect of such training is the use of real negative data. Real negatives matter - they help the model understand what it shouldn't predict, thus improving its accuracy and reducing false positives. In this article, we discuss some effective strategies for continuous training using real negative data, such as active learning, semi-supervised learning, and transfer learning. By using these strategies, you can ensure that your model continues to improve and deliver accurate results.