Summary

In this chapter, we have learned how to use deep learning ConvNets to recognize MNIST handwritten characters with high accuracy. We used the CIFAR-10 dataset to build 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 Inception V3. We concluded with a discussion on transfer learning.

In the next chapter, we’ll see how to work with word embeddings and why these techniques are important for deep learning.