Pytorch for Neural Networks Part 1: Writing Your First Neural Network in Pytorch

A developer has published a tutorial series on building neural networks with PyTorch, starting with the fundamentals of creating a basic neural network class. The first installment demonstrates how to import essential PyTorch modules including torch, torch.nn, torch.nn.functional, and torch.optim.SGD, then walks through constructing a custom neural network class that inherits from nn.Module.

In my previous series of articles, we mainly explored the theory behind various neural network concepts . In this new series, we will focus on putting that knowledge into practice using code . This will be a fun way to turn what we have learned into something more practical. We will start with the basics and build things step by step. For this article, we will be using the following modules. python import torch torch is used to create tensors , which store all the numerical data in neural networks, such as: python import torch.nn as nn This module helps us define and build neural network components. It also allows us to make weights and biases part of the neural network. python import torch.nn.functional as F This module gives us access to various activation functions and other useful operations. python from torch.optim import SGD SGD , which stands for Stochastic Gradient Descent , is an optimization algorithm used to fit the neural network to data. Now let us begin building our neural network. When creating a neural network in PyTorch, we usually start by creating a class. class MyBasicNN nn.Module : Here, we create a class named MyBasicNN . This class inherits from a PyTorch class called nn.Module . By inheriting from nn.Module , our class gains all the functionality needed to behave like a neural network in PyTorch. Next, we define the initialization method. python class MyBasicNN nn.Module : def init self : super . init Here, we define the constructor init for our neural network. The line: super . init calls the initialization method of the parent class nn.Module . This ensures that all the necessary PyTorch functionality is properly set up for our neural network. The next step is to initialize the weights and biases for our neural network. Before doing that, we first need an example problem so we know what kind of neural network we want to build. We will explore that in the next article. AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production. git-lrc https://github.com/HexmosTech/git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free. Any feedback or contributors are welcome It's online, source-available, and ready for anyone to use. Give it a ⭐ star on Github https://github.com/HexmosTech/git-lrc