Convolutional Neural Networks in TensorFlow through Keras

Between this and the previous chapter, we have come quite a long way covering the most important topics in deep learning. We now understand how to construct architectures by stacking multiple layers in a neural network and how to discern and utilize backpropagation methods. We also covered the concept of unsupervised pretraining with stacked and denoising autoencoders. The next and really exciting step in deep learning is the rapidly evolving field of Convolutional Neural Networks (CNN), a method of building multilayered, locally connected networks. CNNs, commonly referred to as ConvNets, are so rapidly evolving at the time of writing this book that we literally had to rewrite and update this chapter within a month's timeframe. In this chapter, we will cover the most fundamental and important concepts behind CNNs so that we will be able to run some basic examples without becoming overwhelmed by the sometimes enormous complexity. However...