Have you ever wondered how deep learning models classify data into multiple categories? I recently built a softmax classification model using TensorFlow and Keras to explore this very concept — and the results were both visually and technically rewarding!

In this project, I worked with a custom 2D dataset containing data points labeled into 10 different classes (0–9). My goal was to train a model that could not only accurately classify the data but also visually demonstrate the decision boundaries created by the softmax layer.

🔍 What the Model Does:

• Takes 2 features as input

• Uses a hidden layer with ReLU activation

• Outputs predictions through a softmax layer (for 10 classes)

• Trains the model and visualizes decision boundaries

📊 What I Learned:

• How to handle label mismatches (e.g., label "9" but only 3 output units? Oops!)

• Why softmax activation is ideal for multi-class problems

• The importance of good data visualization before and after training

🛠️ Technologies Used:

• Python

• TensorFlow / Keras

• NumPy

• Matplotlib

📁 Check It Out on GitHub:

🔗 GitHub Repository:
https://github.com/AdnanCodes-hub/DEEPLEARNING.SOFTMAX

Feel free to explore the code, run the model, and even experiment with your own dataset. If you're a beginner in machine learning or looking to reinforce your knowledge of classification, this project can be a great reference.

Let me know your thoughts or feedback — and happy coding! 💻🧠