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Classroom Contents
What You Always Wanted to Know About Deep Learning, but Were Afraid to Ask
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- 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
