LSTMs and GRUs focuses on advanced recurrent neural network architectures designed to handle sequential and time-dependent data effectively. It enables models to learn long-term dependencies in data, improving performance in tasks like language modeling and time-series prediction. This training explains Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures, including their gating mechanisms and internal workflows. It also covers applications in natural language processing, speech recognition, and forecasting systems. You will learn how organizations use LSTMs and GRUs to solve complex sequence modeling problems. The course also highlights best practices for designing and optimizing recurrent neural network models.
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