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Author: habibrahmanbd

answers/7.1.basics.md

@@ -285,5 +285,4 @@
       - If there is bias in the data used to train the model, use techniques such as re-sampling or adversarial training to reduce bias.
       - If limited resources are causing poor performance, consider using techniques such as model compression, quantization, and distillation to reduce the size and complexity of the model.
       - If the data in production is changing, use techniques such as online learning to adapt the model to the changing data.
-      - It is important to note that it is often a combination of factors that leads to poor performance in production, and it may be necessary to address multiple issues simultaneously to improve performance.
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+      - It is important to note that it is often a combination of factors that leads to poor performance in production, and it may be necessary to address multiple issues simultaneously to improve performance.