Regularization in deep learning
Publication Type
Book
Publication Date
12-2024
Abstract
Regularization in Deep Learning teaches you how to improve your model performance with a toolbox of regularization techniques. It covers both well-established regularization methods and groundbreaking modern approaches. Each technique is introduced using graphics, illustrations, and step-by-step coding walkthroughs that make complex math easy to follow. You’ll learn how to augment your dataset with random noise, improve your model’s architecture, and apply regularization in your optimization procedures. You’ll soon be building focused deep learning models that avoid sprawling complexity and deliver more accurate results even with new or messy data sets.
Discipline
Categorical Data Analysis | Finance and Financial Management
Research Areas
Quantitative Finance
First Page
1
Last Page
275
Publisher
CRC Press
City or Country
Boca Raton, FL
Citation
LIU, Peng.
Regularization in deep learning. (2024). 1-275.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/7202
Comments
Forthcoming with CRC Press