Building a neural network using PyTorch

In the previous chapter, we learned about building a neural network from scratch, where the components of a neural network are as follows:

• The number of hidden layers
• The number of units in a hidden layer
• Activation functions performed at the various layers
• The loss function that we try to optimize for
• The learning rate associated with the neural network
• The batch size of data leveraged to build the neural network
• The number of epochs of forward and back-propagation

However, for all of these, we built them from scratch using NumPy arrays in Python. In this section, we will learn about implementing all of these using PyTorch on a toy dataset. Note that we will leverage our learning so far regarding initializing tensor objects, performing various operations on top of them, and calculating the gradient values to update weights when building a neural network using PyTorch.