Gender Classification Using Neural Networks

This project demonstrates the implementation of a fully connected neural network to classify gender based on facial images. Using the UTKFace dataset, which comprises over 20,000 aligned and cropped face images representing a diverse range of ages (from 0 to 116 years) and ethnic backgrounds, this project aims to build and test a simple yet effective neural network for accurate gender classification.

Key Features:

Flexibility: neural network can easily be configured with any number of hidden layers and neurons specified in the hidden_layers list.

Ability to Choose Activation Function: You can choose to use either the ReLU or Sigmoid activation function for the hidden layers.