Building a Neural Network from Scratch in Java

🚀 Work 1:1 with a Software Engineer and automate everything you hate doing → https://www.skool.com/ai-academy-with-robby-6849/about
Email Me 📧:
robbyj1925@icloud.com
Website👨🏽💻:
https://robj1925.github.io/about-me/

In this video, I walk you step-by-step through building a fully working neural network from scratch in Java. No libraries, no shortcuts—just pure Java code implementing the math behind forward passes, activation functions, backpropagation, gradient descent, and weight updates.

To make the example practical, we use the classic XOR problem, a nonlinear challenge that cannot be solved with a simple linear model. This makes it the perfect test for a small neural network with a hidden layer.

You’ll see exactly how to:

• Structure a 3-layer neural network (input → hidden → output)
• Initialize weights, biases, and learning rate
• Build the forward pass using weighted sums + sigmoid activation
• Implement backpropagation to compute errors
• Update weights and biases with gradient descent
• Train the network for 10,000 epochs
• Evaluate predictions and watch the model learn over time

By the end, you’ll fully understand how a neural network learns under the hood and how Java can be used to implement foundational machine learning concepts without relying on ML frameworks.

This is a great project for anyone learning AI, Java, neural networks, machine learning fundamentals, or preparing for interviews.

https://github.com/Code-With-Robby/Neural_Network_From_Scratch_Java_Demo/blob/main/NeuralNetwork.java
00:00 – Building a Neural Network from Scratch in Java (XOR Problem Overview)
00:26 – Neural Network Architecture Explained (Inputs, Hidden Layer, Sigmoid, Weights, Biases)
00:46 – Forward Pass and Backpropagation in Java (Training Logic + Gradient Descent)
06:44 – Results, Training Analysis, and Final XOR Predictions

Видео Building a Neural Network from Scratch in Java канала Code With Robby🤖