Feedforward Neural Network

Implemented a neural network using Python. The implementation includes key features such as model architecture, training, evaluation, and optimization. The project is structured to be modular and easily extensible.

Features

• Customizable Model Architecture
• Implemented training loops with backpropagation and optimizers
• Hyperparameter Tuning
• Batch size modification and optimization techniques (ADAM optimizer)

Model Definition

• Input Layer
• Hidden Dense Layers (with ReLU as activation function)
• Dropout Layer (for regularization)
• Output Layer (for classification or regression tasks)

Training Process

• Forward propagation
• Loss computation (cross-entropy for classification)
• Backpropagation
• Parameter updates using ADAM optimizer