A Practical Approach to Timeseries Forecasting Using Python - RNN Forecasting

The video tutorial covers an introduction to Recurrent Neural Networks (RNN) and their application in time series forecasting. It discusses key machine learning terms like bias, variance, underfitting, and overfitting. The tutorial includes performance analysis of LSTMs, BI LSTMs, and GRUs, and explores the development and implementation of stacked LSTM models. It also addresses model optimization for improved data performance and highlights the use of RNNs for sequential data. Finally, the video provides an overview of the course project.

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