Deep Learning - Recurrent Neural Networks with TensorFlow - RNN Code Preparation

The video tutorial covers the implementation of a simple RNN using TensorFlow 2.0. It begins with an introduction to the concept of a simple RNN, emphasizing that 'simple' is part of the name rather than a description of complexity. The tutorial outlines the basic steps for creating an RNN script, including data loading, model instantiation, training, evaluation, and making predictions. It highlights the importance of data preparation, particularly the need to reshape data for RNN input. The tutorial also explains how to instantiate the model using TensorFlow's functional API, train it using mean squared error and the Adam optimizer, and make predictions while paying attention to input and output shapes.

Describe the process of compiling the model in the context of this RNN.

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What is the expected output shape of the RNN model when performing scalar regression?

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How do you reshape the input for the RNN to ensure it has the correct dimensions?

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