Deep Learning for Time Series Cookbook
Completed by ALEENA SALEEM
August 4, 2026
7 hours (approximately)
ALEENA SALEEM's account is verified. Coursera certifies their successful completion of Deep Learning for Time Series Cookbook
What you will learn
Implement deep learning models in PyTorch for forecasting and classification of time series data.
Transform raw time series into formats suitable for neural networks and transformer architectures.
Detect anomalies and unusual patterns using autoencoders and GAN-based approaches.
Skills you will gain
- Category: Deep Learning
- Category: Forecasting
- Category: Anomaly Detection
- Category: Data Preprocessing
- Category: Recurrent Neural Networks (RNNs)
- Category: Model Training
- Category: Time Series Analysis and Forecasting
- Category: Predictive Modeling
- Category: Autoencoders
- Category: Model Optimization
- Category: PyTorch (Machine Learning Library)
- Category: Data Architecture
