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Jeremy from AI Expedition explains how to use TensorFlow records and dataset pipelines to improve training code efficiency. Working with TensorFlow records may seem daunting at first, but it is manageable once you understand the process. TensorFlow records are particularly useful when dealing with large datasets that cannot fit into memory. By employing batching techniques, you can optimize the training process. Jeremy demonstrates both a basic implementation and a more advanced approach using TensorFlow records. The tutorial focuses on building an image classification project as an example.
