Deep Neural Network Basics for Keras and TensorFlow

Explore the fundamentals of deep neural networks in this 23-minute video lecture, focusing on key concepts for Keras and TensorFlow implementation. Learn about activation functions like ReLU, bias neurons, connections, and summation techniques. Discover how to calculate neural network outputs and gain insights into essential components such as sigmoid and hyperbolic tangent functions, gradient descent, and the vanishing gradient problem. This lecture is part of a comprehensive course on deep learning applications taught at Washington University in St. Louis, but is accessible to online learners. Dive into neural network basics and prepare to enhance your understanding of this powerful machine learning technology.

Introduction
Neural Network Basics
Neural Networks
Activation Functions
Sigmoid
hyperbolic tangent
gradient descent
vanishing gradient
bias neurons
bias shifts