Introduction to convolutional neural network using Keras; An understanding from a statistician

Hagyeong Lee, Jongwoo Song

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

Deep Learning is one of the machine learning methods to find features from a huge data using non-linear transformation. It is now commonly used for supervised learning in many fields. In particular, Convolutional Neural Network (CNN) is the best technique for the image classification since 2012. For users who consider deep learning models for real-world applications, Keras is a popular API for neural networks written in Python and also can be used in R. We try examine the parameter estimation procedures of Deep Neural Network and structures of CNN models from basics to advanced techniques. We also try to figure out some crucial steps in CNN that can improve image classification performance in the CIFAR10 dataset using Keras. We found that several stacks of convolutional layers and batch normalization could improve prediction performance. We also compared image classification performances with other machine learning methods, including K-Nearest Neighbors (K-NN), Random Forest, and XGBoost, in both MNIST and CIFAR10 dataset.

Original languageEnglish
Pages (from-to)591-610
Number of pages20
JournalCommunications for Statistical Applications and Methods
Volume26
Issue number6
DOIs
StatePublished - 2019

Keywords

  • CIFAR10
  • Convolutional neural network
  • Deep neural network
  • Image classification
  • Keras
  • Machine learning
  • MNIST

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