Implementing a neural network from scratch in C++ to perform XOR operations presented a fascinating challenge. Although XOR is a simple operation, constructing a neural network without any external libraries required a thorough understanding of neural networks and their fundamental components.

Core Components of Neural Networks:

1. Input Layer: Where the network receives its input data.
2. Hidden Layers: Intermediate layers where computations are performed using weights and activations.
3. Output Layer: Produces the network’s output.
4. Weights and Biases: Adjustable parameters of the network that are refined during training.
5. Activation Function: Function that introduces non-linearities into the output of a neuron.

Steps in the Learning Process:

1. Forward Propagation: Passing input data through the network to get an output.
2. Loss Calculation: Assessing the accuracy of the output by comparing it to the expected result.
3. Backpropagation: Adjusting the model parameters based on the output error.
4. Weight Update: Optimizing the weights and biases using algorithms like gradient descent.

This project required delving into various aspects of machine learning, from algorithmic implementation to the intricacies of mathematical models underlying the neural network operations. The exercise was not only about programming but also involved significant theoretical learning, enhancing my understanding of both the practical and theoretical aspects of neural networks.