Having established the fundamentals of neural network components, data preparation, forward propagation, and the learning mechanisms of backpropagation and gradient descent, we now proceed to assemble these elements. This chapter focuses on the practical construction and training process of a basic neural network from start to finish.

You will learn how to:

• Define a network's architecture, specifying layers and activation functions.
• Apply appropriate techniques for initializing the network's parameters (weights $W$ and biases $b$).
• Structure and implement the iterative training loop, processing data in epochs and batches.
• Combine forward pass, loss calculation (e.g., using Mean Squared Error or Cross-Entropy), backpropagation to find gradients like $\frac{\partial L}{\partial W}$, and parameter updates using gradient descent.
• Monitor key metrics such as loss and accuracy to gauge training progress.
• Get a brief overview of how deep learning libraries facilitate building these models.

The chapter concludes with a hands-on exercise where you will apply these steps to build and train a simple neural network classifier using Python.