Summary
Traditional neural networks fail when new images that are very similar to previously seen images that have been translated are fed as input to the model. CNNs play a key role in addressing this shortcoming. This is enabled through the various mechanisms that are present in CNNs, including filters, strides, and pooling. Initially, we built a toy example to learn how CNNs work. Then, we learned how data augmentation helps in increasing the accuracy of the model by creating translated augmentations on top of the original image. After that, we learned about what different filters learn in the feature learning process so that we could implement a CNN to classify images.
Finally, we saw the impact that differing amounts of training data have on the accuracy of test data. Here, we saw that the more training data that is available, the better the accuracy of the test data. In the next chapter, we will learn how to leverage various transfer learning techniques to increase the accuracy...
