“How Deep Neural Networks Work” is a comprehensive course designed to help beginners understand the principles and mechanics behind deep learning. The course starts with an introduction to neural network fundamentals, covering neurons, layers, activation functions, and the flow of data through the network. Students learn how deep networks process complex data, and how forward propagation computes predictions. Backpropagation and gradient descent are explained intuitively to show how networks adjust weights and minimize errors during training. The course also covers optimization techniques, overfitting, regularization, and practical tips for building robust models. Through hands-on Python exercises, learners build, train, and test deep neural networks for real-world applications such as image recognition, speech processing, and predictive analytics. By the end of this course, participants will understand the internal workings of deep networks and gain the practical skills needed to implement and fine-tune models in real projects. This course is ideal for beginners, aspiring AI developers, and anyone looking to strengthen their foundational knowledge in deep learning.