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Welcome to Knet.jl’s documentation!

Contents:

• Setting up Knet
  • Installation
  • Tips for developers
  • Using Amazon AWS
• Introduction to Knet
  • Contents
  • Installation
  • Examples
  • Benchmarks
  • Function reference
  • Optimization methods
  • Under the hood
  • Contributing
• Backpropagation
  • Partial derivatives
  • Chain rule
  • Multiple dimensions
  • Multiple instances
  • Stochastic Gradient Descent
  • References
• Softmax Classification
  • Classification
  • Likelihood
  • Softmax
  • One-hot vectors
  • Gradient of log likelihood
  • MNIST example
  • Representational power
  • References
• Multilayer Perceptrons
  • Stacking linear classifiers is useless
  • Introducing nonlinearities
  • Types of nonlinearities (activation functions)
  • Representational power
  • Matrix vs Neuron Pictures
  • Programming Example
  • References
• Convolutional Neural Networks
  • Motivation
  • Convolution
  • Pooling
  • Normalization
  • Architectures
  • Exercises
  • References
• Recurrent Neural Networks
  • References
• Reinforcement Learning
  • References
• Optimization
  • References
• Generalization
  • References

Indices and tables

• Index
• Module Index
• Search Page