Deep Neural Networks

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.

Introduction
Recap
Functions
Neural Networks
Activation Functions
Approximation Theorem
Optimization
Deep Neural Networks
Depth Matters
Gradient Vanishing
Solutions
rectified linear units