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Feb 23, 2024
By: Bhumi Shrivastava
Having good data is crucial for AI to perform well. However, managing the balance between having enough data and ensuring its quality is essential.
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Data preprocessing involves tasks like removing noise, standardizing formats, and transforming data to make it suitable for training.
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Techniques such as rotation, translation, scaling, and adding noise can help the AI generalize better and produce more varied outputs.
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Providing labels or annotations for the data is important for supervised learning tasks.
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AI models can inadvertently learn biases present in the training data, leading to biased outputs. It's crucial to address bias by thoroughly examining the dataset.
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Given the sensitive nature of some data used in AI models, it's important to implement measures to protect privacy and ensure security.
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Mastering data management is vital for unleashing the full potential of generative AI, ensuring both quality outputs and ethical practices.
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