Completed
Categories of Activation Functions
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
What You Always Wanted to Know About Deep Learning, but Were Afraid to Ask
Automatically move to the next video in the Classroom when playback concludes
- 1 Intro
- 2 Agenda
- 3 AI, Machine Learning, and Deep Learning
- 4 What is Deep Learning?
- 5 Implementing Deep Learning using Neural Networks Outputs
- 6 Inputs and Outputs in a Neural Network
- 7 Hidden Layer(s)
- 8 Weights and Biases
- 9 Calculating the Result of a Node (Forward Propagation)
- 10 Feeding the Result of a Node to an Activation Function
- 11 Categories of Activation Functions
- 12 Binary Step Function
- 13 Analogy
- 14 Use of Sigmoid Activation
- 15 Non-Linear Activation
- 16 Evaluating Performance
- 17 Cross Entropy
- 18 In Summary Activation Function and Loss Function
- 19 Using an Optimizer
- 20 Back Propagation
- 21 A walkthrough
- 22 Initializing the Weights
- 23 Significance of the Partial Differentials
- 24 Updating the Weights using Stochastic Gradient Descent
- 25 In Summary Activation Function, Optimizer, and Loss Function
- 26 TensorFlow and Keras
