Recurrent Neural Networks are designed to handle sequential data by incorporating the essential dimension of time. This type of data appears everywhere from the prediction of stock prices to the modelling of language, so it’s an essential skillset for someone interesting in getting into deep learning. This article will cover:

• A demonstration of properly vectorizing data for a sequential model
• A discussion on how to shape data for recurrent neural networks
• Implementation of RNNs, LSTMs, GRUs, and Embeddings
• Best practices and tips in building effective deep models

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