- Published on June 18, 2020
- In Deep Tech
Hands-on Guide To Create Ensemble Of Convolutional Neural Networks
In this article, we will create an ensemble of convolutional neural networks. In this experiment, we will create an ensemble of 10 CNN models and this ensemble will be applied in multi-class prediction of MNIST handwritten digit data set.
- By Dr. Vaibhav Kumar
Convolutional Neural Networks have proven their advantage as a deep learning model in a variety of applications. When handling the large data sets to extract features and make predictions, the CNN models have always shown their competency. In the majority of the applications, one individual CNN model is applied. Now, there is always a scope to use a group of CNN models in the same tasks as an ensemble learning approach. In one of our articles, we discussed the customization of ensemble learning models and have seen their increased efficiency. Now, let us tune this ensembling approach with CNN models. If successful, it can be applied to the tasks where CNN models have given a low accuracy as per expectations.
In this article, we will create an ensemble of convolutional neural networks. In this experiment, we will create an ensemble of 10 CNN models and this ensemble will be applied in multi-class prediction of MNIST handwritten digit data set. Initially, we will define the individual CN
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Dr. Vaibhav Kumar
Dr. Vaibhav Kumar is a seasoned data science professional with great exposure to machine learning and deep learning. He has good exposure to research, where he has published several research papers in reputed international journals and presented papers at reputed international conferences. He has worked across industry and academia and has led many research and development projects in AI and machine learning. Along with his current role, he has also been associated with many reputed research labs and universities where he contributes as visiting researcher and professor.
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