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4 courses
60 practices
4 hours
Master time series forecasting with PyTorch. Build and optimize RNNs, LSTMs, and GRUs for univariate and multivariate data. Advance from basic sequences to complex hybrid models and attention mechanisms to solve real-world data science challenges.
Learn to build and evaluate RNN, LSTM, and GRU models for time series forecasting using PyTorch. You'll work with univariate and multivariate data, implement classification tasks, and apply advanced techniques to boost forecasting performance.
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 PyTorch 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.
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