🧠 Neural Network from Scratch - Fashion MNIST Classifier

This repository contains a full implementation of a deep neural network from scratch using NumPy, trained on the Fashion-MNIST dataset. The goal is to classify clothing images into one of 10 categories without using high-level deep learning libraries like TensorFlow or PyTorch.

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📁 Dataset

• The dataset is based on Fashion-MNIST, which is a drop-in replacement for MNIST but contains fashion product images.
• Each image is 28x28 grayscale pixels.
• There are 10 classes:
  • 0: T-shirt/top
  • 1: Trouser
  • 2: Pullover
  • 3: Dress
  • 4: Coat
  • 5: Sandal
  • 6: Shirt
  • 7: Sneaker
  • 8: Bag
  • 9: Ankle boot

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🧩 Features Included

✅ Data Preprocessing

• Normalization: Pixel values are divided by 255.0 to scale them between 0 and 1.
• Standardization: Each feature is zero-centered and scaled to unit variance.
• One-hot encoding: Labels are converted from scalar integers to binary class matrices using OneHotEncoder.

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✅ Activation Functions

• ReLU (Rectified Linear Unit) and its derivative
• Sigmoid and its derivative
• Softmax for multi-class classification in the output layer

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✅ Network Architecture

The architecture is flexible and defined by:

layers = [784, 512, 256, 128, 10]
activation = ['ReLU', 'ReLU', 'ReLU', 'softmax']