Explore the fundamentals of deep neural networks in this comprehensive lecture covering key concepts such as activation functions, approximation theorem, optimization techniques, and the importance of network depth. Learn about the challenges of gradient vanishing and discover solutions like rectified linear units. Gain insights into the power of deep learning architectures and their ability to model complex functions through this in-depth examination of neural network principles.