Introduction
Introduction to LightGBM, a gradient boosting framework that is efficient and scalable for large datasets.
Theory
Understand the theoretical concepts behind LightGBM, including its approach to handling large data and high performance.
Practical Example
Simplify the implementation of LightGBM using scikit-learn with practical examples.
TensorFlow Example
Implement LightGBM models using TensorFlow for high-performance machine learning tasks.
PyTorch Example
Learn how to use LightGBM with PyTorch to leverage its gradient boosting capabilities.
Common Mistakes & Best Practices
Discover common errors in LightGBM usage and learn best practices for model optimization.
Comparison with Other Algorithms
Compare LightGBM with other boosting algorithms to understand its efficiency and scalability.