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Machine Learning with LightGBM and Python

You're reading from Machine Learning with LightGBM and Python A practitioner's guide to developing production-ready machine learning systems

Product type Paperback

Published in Sep 2023

Publisher Packt

ISBN-13 9781800564749

Length 252 pages

Edition 1st Edition

Languages

Python

Tools

LightGBM

Concepts

Machine Learning

Author (1):

Andrich van Wyk

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1: Gradient Boosting and LightGBM Fundamentals

2. Chapter 1: Introducing Machine Learning FREE CHAPTER

3. Chapter 2: Ensemble Learning – Bagging and Boosting

4. Chapter 3: An Overview of LightGBM in Python

5. Chapter 4: Comparing LightGBM, XGBoost, and Deep Learning

6. Part 2: Practical Machine Learning with LightGBM

7. Chapter 5: LightGBM Parameter Optimization with Optuna

8. Chapter 6: Solving Real-World Data Science Problems with LightGBM

9. Chapter 7: AutoML with LightGBM and FLAML

10. Part 3: Production-ready Machine Learning with LightGBM

11. Chapter 8: Machine Learning Pipelines and MLOps with LightGBM

12. Chapter 9: LightGBM MLOps with AWS SageMaker

13. Chapter 10: LightGBM Models with PostgresML

14. Chapter 11: Distributed and GPU-Based Learning with LightGBM

15. Index

Why subscribe?

16. Other Books You May Enjoy

References

[1]	T. Chen and C. Guestrin, “XGBoost,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
[2]	A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, Attention Is All You Need, 2017.
[3]	X. Huang, A. Khetan, M. Cvitkovic, and Z. Karnin, TabTransformer: Tabular Data Modeling Using Contextual Embeddings, 2020.
[4]	R. Becker, Adult, UCI Machine Learning Repository, 1996.
[5]	D. Hendrycks and K. Gimpel, Gaussian Error Linear Units (GELUs), 2020.
[6]	G. Klambauer, T. Unterthiner, A. Mayr and S. Hochreiter, Self-Normalizing Neural...

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Authors (1)

Andrich van Wyk

Andrich van Wyk

Andrich van Wyk has 15 years of experience in machine learning R&D and building AI-driven solutions. He also has broad experience as a software engineer and architect with over a decade of industry experience working on enterprise systems. He graduated cum laude with an M.Sc. in Computer Science from the University of Pretoria. His work focused on neural networks and population-based algorithms such as Particle Swarm Optimization and Honey-Bee Foraging. Andrich also writes about software and machine learning on his blog and his Substack. He currently resides in South Africa with his wife and daughter.

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