The classifier predicts it correctly for the centered image but fails in the other two cases. Given below is an example of the number 7 being pushed to the top-left and bottom-right. We want the network to be Translation-Invariant. If the images in the test set are off-center, then the MLP approach fails miserably. In our training dataset, all images are centered. But there was a problem with that approach. The performance was pretty good as we achieved 98.3% accuracy on test data. In our previous article on Image Classification, we used a Multilayer Perceptron on the MNIST digits dataset. We will use the MNIST and CIFAR10 datasets for illustrating various concepts. We discussed Feedforward Neural Networks, Activation Functions, and Basics of Keras in the previous tutorials. We will also see how data augmentation helps in improving the performance of the network. In this tutorial, we will learn the basics of Convolutional Neural Networks ( CNNs ) and how to use them for an Image Classification task.
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