AI Luminaries
A curated collection of videos featuring top experts in artificial intelligence.
Overview
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.
What you'll learn
- Prepare univariate and multivariate time series data for neural networks
- Build RNN, LSTM, and GRU models in PyTorch
- Train models to forecast values and classify sequential data
- Evaluate forecasting accuracy with appropriate performance metrics
- Apply bidirectional networks and attention mechanisms to time series tasks
- Develop hybrid GRU-CNN models for complex temporal patterns
Advantages and disadvantages
Advantages
- University courses you can audit for free, with lectures, readings and practice quizzes.
- A verified certificate from the university if you pay for it.
- Self-paced: start any time.
Disadvantages
- Graded assignments and the certificate need the paid track.
- Audit access can expire a few weeks after the course ends.
- Learning is free, but the certificate costs money.
- Some parts (graded work, certificate) are paid.
Some points apply to every course of this kind; see how we rank.
Free to audit
- Free: Choose "Audit this course" when you enrol: lectures, readings and practice are free.
- Paid: Graded assignments and the verified certificate (Certificate $30). Audit access may end after the course closes.
Before you start
Beginner: Coding and Data Algorithms (Pandas, Python, PyTorch) Beginner: Machine Learning and Predictive Modeling (Pandas, Python, PyTorch)
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