Forecast using Deep Learning you own this product

prerequisites
intermediate Python • intermediate data science • basic Google Colab • basics of deep learning
skills learned
perform forecasting with LSTM and N-BEATS models using PyTorch Forecasting and PyTorch Lightning • optimize models with Bayesian optimization using Optuna • implement callbacks for customized model training
Jiahao Weng
1 week · 8-10 hours per week · INTERMEDIATE
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Look inside

In this liveProject, you’ll use deep learning to implement powerful time series forecasting models that can beat the performances of previous models. You’ll work with the Python package “PyTorch Forecasting” and the deep learning models LSTM and N-BEATS. You’ll also get experience with key techniques of cross learning, ensembling, and hyperparameter tuning.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Jiahao Weng
Jiahao Weng is a machine learning practitioner and a senior data scientist at a multinational company where he delivers projects ranging from proof-of-concept to production machine learning systems. As a freelance writer, he also contributes data science Medium articles to share his knowledge with the community.

prerequisites

The liveProject series is for intermediate data scientists interested in tackling their first end-to-end machine learning project. To begin this liveProject, you will need to be familiar with the following:


TOOLS
  • Intermediate Python
  • Basic Google Colab
  • Basic Jupyter Notebook
TECHNIQUES
  • Intermediate data science
  • Basics of deep learning

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