This course segment covers the core concepts and practical applications of artificial neural networks. You will learn how neural networks are structured and how they mimic the human brain to process information, including the mechanics of backpropagation and weight adjustments for effective learning. The material delves into real-world applications, demonstrating how these models can be used for classification tasks—such as recognizing handwritten digits or detecting anomalies in medical images—as well as unsupervised clustering to reveal hidden patterns in complex data sets. Additionally, the course addresses strategies for optimizing model performance and overcoming common challenges like overfitting, empowering you with the skills to design, train, and fine-tune neural networks for diverse applications in industries ranging from healthcare to finance.
