SolveWithPython

Neural Networks From Scratch in Python

A Practical, Step-by-Step Learning Hub

Neural networks are often taught backwards.

Most tutorials start with frameworks, abstractions, and prebuilt layers—leaving readers able to use neural networks, but not truly understand them.

This series takes a different approach.

Here, we build a neural network from scratch in pure Python, one concept at a time, with no machine-learning libraries and no hidden steps. Every line of code is explained. Every mathematical idea is grounded in implementation.

This hub page is your starting point and reference map for the entire series.

Who This Series Is For

This series is designed for readers who:

  • Know basic Python (functions, loops, lists)
  • Want to understand how neural networks actually work
  • Feel lost when frameworks abstract everything away
  • Want intuition before optimization
  • Prefer learning by building, not memorizing APIs

You do not need prior machine learning experience.

How the Series Is Structured

Each article:

  • Introduces one core concept
  • Adds one concrete building block
  • Includes runnable Python code
  • Builds directly on the previous article
  • Avoids unnecessary abstraction

By the end, you will have implemented a complete neural network and understand exactly how and why it works.

📘 Core Series: Neural Networks From Scratch (Pure Python)

Article #1

What a Neuron Really Computes

Focus: The mathematical core of a neuron

You start by building a single artificial neuron from first principles:
inputs, weights, bias, and a weighted sum.

This article establishes the foundation for everything that follows.

👉 Read Article #1: What a Neuron Really Computes

Article #2

Activation Functions — Why a Network Without Them Cannot Learn

Focus: Non-linearity and expressive power

You learn why stacking linear neurons does nothing useful, and how activation functions like ReLU and Sigmoid transform a simple calculator into a learning system.

👉 Read Article #2: Activation Functions in Neural Networks

Article #3

Building a Neural Network Layer in Python

Focus: From isolated neurons to structure

This article shows how multiple neurons work together in a dense (fully connected) layer, and how vectors flow into vectors.

This is where the network begins to take shape.

👉 Read Article #3: Building a Neural Network Layer

Article #4

Forward Propagation — How Data Flows Through the Network

Focus: End-to-end computation

You connect multiple layers and implement forward propagation step by step.
For the first time, your network produces an actual prediction.

No learning yet—just clean, deterministic computation.

👉 Read Article #4: Forward Propagation Explained

Article #5 (Coming Next)

Loss Functions — Measuring How Wrong the Network Is

Focus: Quantifying error

You will introduce loss functions such as Mean Squared Error and Cross-Entropy, which allow the network to measure how good or bad its predictions are.

This is the final piece needed before learning can begin.

👉 Coming soon

What You Will Have Built by the End

By completing this series, you will:

  • Understand neurons, layers, and activations deeply
  • Know how forward propagation works internally
  • Be prepared to implement backpropagation
  • Read framework code with confidence
  • Debug neural networks intelligently
  • Recognize when abstractions hide problems

Most importantly, neural networks will stop feeling like “black boxes.”

Code Repository

All code for this series is stored in a single, incrementally built GitHub repository.

Each article adds exactly one new file or concept, so you can follow along without confusion.

👉 View the GitHub repository

How to Use This Hub

Recommended reading order:

  1. Start at Article #1
  2. Read sequentially (each article depends on the previous)
  3. Run the code as you go
  4. Return to this hub whenever you want the big picture

This page will be updated as new articles are added.

What Comes After This Series

After completing the core series, we will move on to:

  • Backpropagation (from scratch)
  • Gradient descent
  • Training loops
  • Debugging neural networks
  • Mapping scratch code to PyTorch
  • Visualizing what neural networks learn

All of these will build directly on what you’ve learned here.

Start Here

If you are new to neural networks, start at the beginning.

👉 Read Article #1: What a Neuron Really Computes