Building a Deep Neural Network
Building a neural network, in general terms, can be achieved either on a very simple level using libraries such as scikit-learn (not suitable for deep learning), which perform all the math for you without much flexibility, or on a very complex level by coding every single step of the training process from scratch, or by using a more robust framework, which allows great flexibility.
PyTorch was built considering the input of many developers in the field and has the advantage of allowing both approximations in the same place. As we mentioned previously, it has a neural network module that was built to allow easy predefined implementations of simple architectures using the sequential container, while at the same time allowing for the creation of custom modules that introduce flexibility to the process of building very complex architectures.
In this section, we will discuss the use of the sequential container for developing deep neural networks in...