In this chapter, you'll be learning how to apply the convolutional neural network (also referred to as the CNN or convnet), perhaps the best-known deep architecture, via the following steps:
- Taking a look at the convnet's topology and learning processes, including convolutional and pooling layers
- Understanding how we can combine convnet components into successful network architectures
- Using Python code to apply a convnet architecture so as to solve a well-known image classification task
In the field of machine learning, there is an enduring preference for developing structures in code that parallel biological structures. One of the most obvious examples is that of the MLP neural network, whose topology and learning processes are inspired by the neurons of the human brain.
This preference has turned out to be highly efficient; the availability of specialized, optimized biological structures that excel at specific...