A Practical Approach to Timeseries Forecasting Using Python - LSTM Implementation and Errors

The video tutorial guides viewers through implementing an LSTM model using TensorFlow. It begins with importing necessary libraries and layers, followed by building a sequential LSTM model. The model is compiled with an Adam optimizer and mean squared error loss function. The tutorial then demonstrates how to fit the model with training data, specifying epochs, batch size, and validation split. Finally, it covers evaluating the model's performance by plotting training and validation loss, and concludes with a brief mention of making predictions.

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