🧠 Deep Learning Demo (Java + DL4J)

A lightweight deep learning demo built in Java using Deeplearning4j (DL4J) . This project demonstrates how to build, train, and visualize a simple neural network — all within a pure Java environment.

🚀 Features

✅ Implements a fully connected neural network using DL4J

📊 Includes a live training UI via UIServer and StatsListener

🧩 Utilizes DataVec for dataset parsing, schema transformation, and preprocessing

⚙️ Configurable layers, activation functions, and optimizers

💾 Easily extendable for your own CSV or in-memory datasets

🧩 Tech Stack

Component Description Language Java Framework Deeplearning4j Data Pipeline DataVec Visualization DL4J UI Server Build Tool Maven / Gradle (depending on your setup)

🧠 How It Works

Loads data — from a CSV file or in-memory collection

Defines a schema and applies transformations via TransformProcess

Builds a neural network using NeuralNetConfiguration and multiple DenseLayers

Trains the model, tracking progress with StatsListener

Launches the DL4J UI to visualize performance and metrics in real time

🏃‍♂️ Running the Project

Prerequisites

Make sure you have:

Java 11+

Maven or Gradle

(Optional) A CSV dataset if you’re experimenting with custom data

Steps

Clone this repository

git clone https://github.com/yourusername/deeplearning-demo.git cd deeplearning-demo

Compile and run

mvn clean install mvn exec:java -Dexec.mainClass="com.deeplearning.App"

Once running, open your browser to:

http://localhost:9000

to view the interactive DL4J Training Dashboard.

🧮 Example Output

During training, the console and UI will display:

Iteration loss (via ScoreIterationListener)

Network accuracy and error

Live plots of learning rate, gradients, and parameters

📈 Future Improvements

Add different activation functions and optimizers

Integrate a real-world dataset (e.g., MNIST or Iris)

Save and load trained models

Add evaluation metrics (precision, recall, F1-score)