© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2022
I. GridinAutomated Deep Learning Using Neural Network Intelligencehttps://doi.org/10.1007/978-1-4842-8149-9_2

2. Hyperparameter Optimization

Ivan Gridin1  
(1)
Vilnius, Lithuania
 

Almost every deep learning model has a large number of hyperparameters. Choosing the proper hyperparameters is one of the most common problems in AutoML. A small change in one of the model’s hyperparameters can significantly change its performance. Hyperparameter Optimization (HPO) is the first and most effective step in deep learning model tuning. Due to its ubiquity, Hyperparameter Optimization is sometimes regarded as synonymous with AutoML.

NNI provides a broad and flexible set ...

Get Automated Deep Learning Using Neural Network Intelligence: Develop and Design PyTorch and TensorFlow Models Using Python now with the O’Reilly learning platform.

O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.