Chapter 1: Modeling Procedure of TensorFlow

    In principle, the neural network could be defined by graphs consist of tensors and trained through automatic differenciate.

    However, for simplification, we recommend to use high-level Keras API in Tensorflow to implement the neural networks.

    The common procedures of implementing neural networks using TensorFlow are:

    1. Data preparation

    2. Model training

    3. Model application

    The most common data types are structured data, images, texts, and temporal sequences.

    We are demonstrating the steps of modeling for these four data types through the following examples, respectively: (1) Predicting the survival on the Titanic; (2) Image classification on CIFAR2 set; (3) Classification of movie reviews on IMDB; (4) Predicting the terminate date of the COVID-19 pandemic in China.

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