In this tutorial we discover how to use TensorFlow Core's low-level APIs to build, train, and evaluate a multiple linear regression model that predicts automobile fuel efficiency
We start by loading the car dataset, defining functions to prepare the data with relevant categories and attributes, then we build the model by adding layers and a custom optimizer
Once the model is ready, we train it with training data, validate its performance, and finally evaluate it with test data to measure accuracy and detect possible improvements
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