Library
Chapter 13
Deep Learning for Time Series
Curated summaries of the key literature behind this chapter — the findings, the methods, and how to put them to work.
25 referenced in this chapter
This chapter Library is for Research-to-Production students
R2P students unlock curated summaries of every key paper behind this chapter — the core findings, the methods, and how to apply them — plus resources we keep adding over time.
Backpropagation through time: what it does and how to do it
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
LSTM can solve hard long time lag problems
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
plus 20 more references inside