El objetivo es examinar las aplicaciones de la Distribución Poisson en casos simulados siguiendo los parámetros con los que el modelo teóricamente predice.

This is an initial exploratory analysis of the text datasets provided for the SwiftKey Capstone Project. The datasets contain English-language data from blogs, news articles, and Twitter posts. The goal is to build a text prediction algorithm and deploy it using a Shiny app.

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Plataforma que muestra las estaciones pluviométricas de México.

One common issue with Recurrent Neural Networks is exploding or vanishing gradients, where the previous state (h(t-1)) and its weight become extremely high, or more commonly are reduced to practically zero during backpropagation. Since we have a lot of data points (1440), this should be addressed. One way to address this is by implementing a Gated Recurrent Unit, which changes the hidden state equation to include an update gate and a reset gate. The update gate balances how much of the new hidden state to incorporate, compared to the previous hidden state. The reset gate sets how much past information to forget when computing the new hidden state. The rest of this model ran similarly to our True RNN, with 10 epochs, measuring MSE and MAE.

Done