Building and Training a Three-Layer Neural Network

Ever wonder what actually happens inside a neural network during training? Demystify deep learning by stripping away complex frameworks and examining the core mathematics and logic from the ground up. Through clear, step-by-step written explanations and practical code examples, you will learn how to design, train, and optimize a three-layer neural network. You will gain a deep, intuitive understanding of how data flows through a network to classify patterns like letters. What you'll learn: Understand the fundamental architecture of a three-layer neural network, including input, hidden, and output layers; Calculate forward propagation step-by-step using activation functions and weight matrices; Implement backpropagation to calculate gradients and update network weights; Train a network to classify letters and simple patterns using clean, modern Python code; Analyze how learning rates and loss functions affect model convergence and performance. This course guides you from basic matrix math and neural definitions to a fully functioning classification model. You will read comprehensive breakdowns of the underlying calculus and implement the logic step-by-step. This course is designed for beginners in machine learning who want to understand the mechanics behind the algorithms, with no prior deep learning experience required. Start reading today to master the core mechanics of neural networks from the inside out.