January 2025
Intermediate to advanced
436 pages
11h 10m
English
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7.2 Integrating Feature Engineering with TensorFlow/Keras
Integrating feature engineering directly into the TensorFlow/Keras workflow offers significant advantages in deep learning model development. This approach transforms the traditional data preparation process by incorporating data transformations directly into the model pipeline. By doing so, it ensures consistency in data preprocessing across both training and inference stages, which is crucial for model reliability and performance.
One of the key benefits of this integration is the enhanced deployment process. When feature engineering steps are embedded within the model, it simplifies the deployment pipeline, reducing the risk of discrepancies between training and production environments. ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.