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We shall train both shallow and deep neural network models and compare them. We use non-linear `ReLU` activation function in the hidden layers of a neural network model to capture the non-linear relationships between features and target.\n\n## Dataset\nWe will use the classic [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) dataset and demonstrates how to build models to predict the fuel efficiency of the late-1970s and early 1980s automobiles. The dataset is also available from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcyprianfusi%2Fpredict-fuel-efficiency-using-linear-regression-with-tensorflow","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcyprianfusi%2Fpredict-fuel-efficiency-using-linear-regression-with-tensorflow","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcyprianfusi%2Fpredict-fuel-efficiency-using-linear-regression-with-tensorflow/lists"}