Building a simple neural network with PyTorch

This section will build a simple two-layer neural network from scratch using only basic tensor operations to solve a time series prediction problem. We aim to demonstrate how one might manually implement a feedforward pass, backpropagation, and optimization steps without leveraging PyTorch’s predefined layers and optimization routines.

Getting ready

We use synthetic data for this demonstration. Suppose we have a simple time series data of 100 samples, each with 10 time steps. Our task is to predict the next time step based on the previous ones:

X = torch.randn(100, 10)
y = torch.randn(100, 1)

Now, let’s create a neural network.

How to do it…

Let’s start by defining our model parameters and their initial values. Here, we are creating a simple two-layer network, so we have two sets of weights and biases:

We use the requires_grad_() function to tell PyTorch that we want to compute gradients with...