Intermediate

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. 1 Table of Contents
  2. 2 General Introduction
  3. 3 Fundamentals of RNNs for time series forecasting
  4. 4 LSTM and GRU models for long time series
  5. 5 From RNNs to Attention and Transformers
  6. 6 Appendices

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