Summary

In this chapter we have learned how to use deep learning convnets for recognizing MNIST handwritten characters with high accuracy. We used the CIFAR-10 dataset for building a deep learning classifier with 10 categories, and the ImageNet dataset to build an accurate classifier with 1,000 categories. In addition, we investigated how to use large deep learning networks such as VGG16 and very deep networks such as InceptionV3. We concluded with a discussion on transfer learning; in the next chapter we'll see how to adapt prebuilt models trained on large datasets so that they can work well on a new domain.