Neural Networks, integral to Artificial Intelligence, consist of interconnected nodes with input, hidden, and output layers, where weights and biases play crucial roles in determining connections and activations. A common challenge in building Deep Neural Networks is managing vanishing and exploding gradients, which can hinder the learning process during backpropagation. To address these issues, initializing weights with small random values and using appropriate activation functions like RELU can prevent neurons from learning the same features and stabilize gradients. The optimization process, typically using Stochastic Gradient Descent, aims to minimize the Cost Function by adjusting weights to enhance predictive accuracy. Proper understanding and application of these techniques are vital for efficient model performance, reducing time, and financial investment in machine learning projects.