Text Generation: Char-RNN Data preparation and TensorFlow implementation

This tutorial provides a step-by-step guide on creating a character-based text generator using a simple two-layer LSTM. The data preparation process involves reading the training data, processing it into numerical representations, and preparing examples for training. The network architecture is designed to learn from these examples and generate new characters based on the input sentence. The model uses cross-entropy loss during training and softmax function during testing. The generated text can be controlled by adjusting the temperature hyperparameter. The tutorial includes code snippets and a demo repository for further exploration.

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