This guide provides a straightforward explanation of how a node is calculated in a fully connected layer within a convolutional network. It highlights the step-by-step process of using weights, biases, and activation functions. Readers will also learn why FC layers are crucial for tasks like image classification and how they differ from other layers. The importance of optimization techniques like dropout is also discussed, offering valuable insights into creating efficient models.