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.
Overview
Syllabus
- Unit 1: Introduction to Time Series Forecasting with LSTMs Using PyTorch
- Tracking Data Shapes in Time Series Preprocessing for LSTM Models
- Exploring the Impact of Sequence Length on Time Series Data Shapes
- Building an LSTM Model for Time Series Forecasting
- Modifying LSTM Hidden Units Configuration
- Unit 2: Building LSTMs for Time Series Forecasting with PyTorch
- Explicit Input Shape Definition and Train-Test Split for LSTM Models
- Fixing Stacked LSTM Layer Configuration in PyTorch
- Evaluating LSTM Model Performance for Time Series Forecasting
- Comparing LSTM Model Performance with Different Training Epochs
- Unit 3: Evaluating LSTM Models with PyTorch
- Calculating and Visualizing RMSE for LSTM Time Series Forecasting
- Adding Mean Absolute Error to Time Series Model Evaluation
- Implementing MAPE for Time Series Model Evaluation
- Enhancing Time Series Forecast Visualizations with Performance Metrics
- Unit 4: Optimizing LSTM Models for Time Series Forecasting with PyTorch
- Adding Dropout to LSTM Models to Prevent Overfitting
- Implementing Regularization Techniques in LSTM Networks for Time Series Forecasting
- Implementing Batch Normalization in LSTM Networks for Improved Training Stability
- Implementing Early Stopping in PyTorch for Time Series Forecasting
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