Awesome Easy-to-Use Deep Time Series Modeling based on PaddlePaddle, including comprehensive functionality modules like TSDataset, Analysis, Transform, Models, AutoTS, and Ensemble, etc., supporting versatile tasks like time series forecasting, representation learning, and anomaly detection, etc., featured with quick tracking of SOTA deep models.

End-to-end ML pipeline predicting TV ad memorability from EEG brain signals. Compares handcrafted spectral features vs. deep temporal embeddings (TS2Vec, FEMBA) for unaided recall classification. MSc thesis — Politecnico di Milano.

Develop a self-supervised learning algorithm to extract deep features from millions of unlabeled signal data to identify abnormal driving behaviors