Recurrent Neural Networks (RNNs)
2. Natural management of variable length sequences.
The goal is to build a strong intuition about how recurrent models handle sequential data, so that you can make good choices when deploying sequence models on real-world forecasting problems.
Throughout this training, the common thread is a concrete use case: you are part of a team responsible for building a forecast model for electricity demand. You have a dataset of energy consumption measured over time, and your mission is to predict what the next load will be so that the operational department can plan in advance.
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What's inside
6 sections- 1 Table of Contents
- 2 General Introduction
- 3 Fundamentals of RNNs for time series forecasting
- 4 LSTM and GRU models for long time series
- 5 From RNNs to Attention and Transformers
- 6 Appendices
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