---
title: "Neural Networks Explained: What They Are and How to Build One in Python"
url: https://daily.dev/posts/neural-networks-explained-what-they-are-and-how-to-build-one-in-python-4lpifrsbi
source_url: https://www.freecodecamp.org/news/neural-networks-explained-simply-in-python
type: article
source: "freeCodeCamp"
published: 2026-08-22T08:20:16.698Z
updated: 2026-08-22T08:20:39.001Z
tags: ["python", "deep-learning", "neural-networks", "pytorch", "numpy"]
reading_time: 31
upvotes: 2
comments: 0
language: en
---

> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Neural Networks Explained: What They Are and How to Build One in Python

**[freeCodeCamp](https://daily.dev/sources/freecodecamp)** · 31 min read · 2 upvotes · 0 comments

## Summary

A beginner-friendly walkthrough builds understanding of neural networks from the ground up, starting with a single artificial neuron and progressing to weights, biases, activation functions, forward propagation, loss, gradients, backpropagation, and gradient descent. Using only Python and NumPy, it constructs a small neural network from scratch to solve the classic XOR problem, then rebuilds the same network in far fewer lines using PyTorch, comparing the manual math against the framework-assisted approach. It closes with an overview of where neural networks are applied (computer vision, NLP, speech, recommendations, generative AI) and suggests a learning path toward deep learning.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.freecodecamp.org/news/neural-networks-explained-simply-in-python>

## Questions this post answers

### How do I build a simple neural network from scratch using only Python and NumPy?

Initialize weight matrices and biases with numpy, run forward propagation using matrix multiplication (X @ W1 + b1) followed by tanh and sigmoid activations, compute binary cross-entropy loss, then backpropagate gradients manually (dz2 = a2 - y, dW2 = a1.T @ dz2 / len(X), etc.) and update parameters using gradient descent with a learning rate like 0.1 over thousands of epochs.

_See more from-scratch machine learning walkthroughs curated for developers on daily.dev._

### Why does a neural network need a hidden layer to learn the XOR function?

XOR cannot be represented by a single linear layer because it is not linearly separable, so a hidden layer with a nonlinear activation function like tanh is required to give the network the flexibility to transform inputs into a representation where the output layer can separate the classes. Without that nonlinearity, connecting inputs directly to the output cannot capture the XOR pattern.

_Developers debugging network architecture choices can follow deep learning explainers on daily.dev._

### What is the difference between implementing backpropagation manually in NumPy versus using PyTorch's autograd?

Manually implementing backpropagation in NumPy requires computing every gradient by hand, such as dz2 = a2 - y, dW2 = (a1.T @ dz2) / len(X), and applying the tanh derivative (1 - a1**2), then updating parameters with explicit subtraction. PyTorch automates this with loss.backward() and optimizer.step(), using an optimizer like Adam, reducing the same logic to a handful of lines.

_Compare framework trade-offs like this by following PyTorch and NumPy discussions on daily.dev._

## Similar posts on daily.dev

- [Neural Networks, Explained for Beginners: Start Here If They’ve Confused You](https://daily.dev/posts/neural-networks-explained-for-beginners-start-here-if-they-ve-confused-you-acznz44sz) · Towards Data Science · 0 upvotes · 0 comments
- [AI Fundamentals: What is a Neuron?](https://daily.dev/posts/ai-fundamentals-what-is-a-neuron--grwe15zt3) · Medium · 1 upvotes · 0 comments

---

Tags: [#python](https://daily.dev/tags/python), [#deep-learning](https://daily.dev/tags/deep-learning), [#neural-networks](https://daily.dev/tags/neural-networks), [#pytorch](https://daily.dev/tags/pytorch), [#numpy](https://daily.dev/tags/numpy)

[View this post on daily.dev](https://daily.dev/posts/neural-networks-explained-what-they-are-and-how-to-build-one-in-python-4lpifrsbi)