Building a shallow neural network from scratch provides a hands-on understanding of its architecture and functionality. This article would guide readers through the process of implementing a simple neural network using Python and a library like NumPy or TensorFlow. It would cover topics such as initializing weights, defining the activation function, implementing forward and backward propagation, and training the model on a small dataset (e.g., the XOR problem or a simple classification task). The focus would be on practical application and clear explanations of each step, allowing readers to solidify their understanding of shallow networks by building one themselves. This practical experience helps demystify the theoretical concepts and sets the stage for understanding more complex deep learning models later on. It allows the reader to test and experiment with different parameters (learning rate, number of neurons) .