Classifying Images with Deep Convolutional Neural Networks

In the previous chapter, we looked in depth at different aspects of the TensorFlow API, you became familiar with tensors and decorating functions, and you learned how to work with TensorFlow Estimators. In this chapter, you will now learn about convolutional neural networks (CNNs) for image classification. We will start by discussing the basic building blocks of CNNs, using a bottom-up approach. Then, we will take a deeper dive into the CNN architecture and explore how to implement CNNs in TensorFlow. In this chapter, we will cover the following topics:

• Convolution operations in one and two dimensions
• The building blocks of CNN architectures
• Implementing deep CNNs in TensorFlow
• Data augmentation techniques for improving the generalization performance
• Implementing a face image-based CNN classifier for predicting the gender of a person