pytorch feed forward neural network example

Certainly! In this tutorial, we will walk through the process of building a simple feedforward neural network using PyTorch. We'll cover the essential steps, including defining the network architecture, preparing the data, training the model, and evaluating its performance. Let's get started!
If you haven't already installed PyTorch, you can do so using the following command:
In your Python script or Jupyter notebook, start by importing the necessary libraries:
Create a class that represents your neural network. For this example, we'll create a simple network with one hidden layer:
For demonstration purposes, let's generate some random data:
Choose a loss function and an optimizer. For simplicity, we'll use Mean Squared Error (MSE) loss and Stochastic Gradient Descent (SGD) optimizer:
Now, train the neural network using the prepared data:
After training, you can evaluate the model on new data:
This tutorial provides a basic example of creating, training, and evaluating a feedforward neural network using PyTorch. Feel free to customize the architecture, hyperparameters, and data based on your specific use case.
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