Volume 3: Deep Learning and Neural Networks

• Chapter 1: Neural Network Basics
  • Activation Functions
  • Derivative of the Sigmoid Function
  • Single Layer Network
• Chapter 2: Self Organizing Maps
  • SOM Colors
• Chapter 3: Hopfield & Boltzmann Machines
  • Hopfield Network
  • Boltzmann Machine TSP Weight Grid
• Chapter 4: Feedforward Neural Networks
  • Softmax
  • Multi Layer Network
• Chapter 5: Training & Evaluation
  • Annealing and ROC
• Chapter 6: Backpropagation Training
  • XOR Neural Network - Analyze Online
  • XOR Neural Network - Analyze Batch
  • XOR Neural Network - Analyze Resilient Propagation(RPROP)
  • XOR Neural Network
  • Classify Neural Network
  • Benchmark Neural Network
• Chapter 7: Other Propagation Training
• Chapter 8: NEAT, CPPN and HyperNEAT
• Chapter 9: Deep Learning
  • ReLu Activation Function
• Chapter 10: Convolutional Neural Networks
  • Convert Image to Textual Numbers
• Chapter 11: Pruning and Model Selection
  • Grid Search Iterations
  • Random Search Iterations
• Chapter 12: Dropout and Regularization
• Chapter 13: Time Series and Recurrent Networks
• Chapter 14: Architecting Neural Networks
• Chapter 15: Visualization
• Chapter 16: Modeling with Neural Networks