In this chapter, we learned how to use Deep Learning ConvNets for recognizing MNIST handwritten characters with high accuracy. Then we used the CIFAR 10 dataset to build a deep learning classifier in 10 categories, and the ImageNet datasets to build an accurate classifier in 1,000 categories. In addition, we investigated how to use large deep learning networks such as VGG16 and very deep networks such as InceptionV3. The chapter concluded with a discussion on transfer learning in order to adapt pre-built models trained on large datasets so that they can work well on a new domain.
In the next chapter, we will introduce generative adversarial networks used to reproduce synthetic data that looks like data generated by humans; and we will present WaveNet, a deep neural network used for reproducing human voice and musical instruments with high quality.
