Introduction to RNN for Time Series Forecast with TensorFlow

This course extends the concepts from the first course by introducing multiple time series inputs. It covers how to preprocess, structure, and train RNN models using **two related time series features** from the **Air Quality dataset**. It also includes model evaluation techniques to assess forecasting accuracy.

This course focuses on LSTM networks, a powerful extension of RNNs that handle long-term dependencies better than simple RNNs. Learners will build, optimize, and evaluate LSTM models for time series forecasting.

This course explores Gated Recurrent Units (GRUs) in TensorFlow for multivariate time series forecasting. We will build, evaluate, and apply advanced GRU techniques like Bidirectional GRUs, Attention Mechanisms, and Hybrid GRU-CNN models to improve forecasting accuracy.