Manual Implementation of Neural Network: The entire neural network is built using Python’s basic libraries, with no use of high-level frameworks. Backpropagation and Gradient Descent: Implemented from scratch to train the neural network. Customizable Architecture: Flexible to adjust the number of layers, neurons per layer, activation functions, and other parameters. Example Use Case: Demonstration of training the neural network on a dataset (e.g., MNIST, XOR problem, or any other simple dataset).