Building a Neural Network From Scratch Using Python (Part 1)

The article provides a comprehensive tutorial on building a two-layer neural network from scratch, focusing on its application to a binary classification task using a heart disease dataset. It begins by explaining fundamental concepts such as artificial intelligence, deep learning, neural networks, and the significance of neural networks in automating tasks through learning weights and biases. The tutorial walks the reader through setting up the data, initializing weights, and understanding activation functions, specifically using ReLU and sigmoid functions. It details forward propagation, which involves calculating weighted sums and outputs, and backpropagation, which updates weights based on loss function derivatives. The article emphasizes the importance of understanding these processes to improve model prediction capabilities and concludes by encouraging readers to test and validate their neural network's performance.

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