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NeuPy is a Python library for Artificial Neural Networks.

$ pip install neupy

• Documentation
• Issues
• Tutorials
• Available algorithms

• Password recovery
• Discrete Hopfield Network
• Boston house-prices dataset
• Visualize Backpropagation Algorithms

• MNIST, Multilayer perceptron
• Rectangle images, Multilayer perceptron
• MNIST, Denoising Autoencoder
• Boston House Price prediction, Hessian algorithm
• Learning Algorithms Visualization, Gradient Descent, Momentum, RPROP and Conjugate Gradient
• IMDB review classification using CBOW and RPROP MLP

• MNIST CNN
• CIFAR10 CNN

• Simple SOFM example

• Classify iris dataset, Probabilistic Neural Network (PNN)
• Regression using Diabetes dataset, Generilized Neural Nerwork (GRNN)
• Music-Speech audio classification, Probabilistic Neural Network (PNN)

• Sinus function approximation, CMAC
• Visualize Discrete Hopfield Neural Network energy function
• Password recovery, Discrete Hopfield Neural Network

• Python 2.7, 3.4
• Theano >= 0.8.1
• NumPy >= 1.9.0
• SciPy >= 0.14.0
• Matplotlib >= 1.4.0

• Adding reccurent neural network layers (Issue #57)
• Bug fixing and version stabilization (Known bugs)
• Speeding up algorithms
• Adding more algorithms

• Algorithms that use Backpropagation training approach
  • Classic Gradient Descent
  • Mini-batch Gradient Descent
  • Conjugate Gradient
    • Fletcher-Reeves
    • Polak-Ribiere
    • Hestenes-Stiefel
    • Conjugate Descent
    • Liu-Storey
    • Dai-Yuan
  • quasi-Newton with Wolfe search
    • BFGS
    • DFP
    • PSB
    • SR1
  • Levenberg-Marquardt
  • Hessian (Newton's method)
  • Hessian diagonal
  • Momentum
  • Nesterov Momentum
  • RPROP
  • iRPROP+
  • QuickProp
  • Adadelta
  • Adagrad
  • RMSProp
  • Adam
  • AdaMax
• Algorithms that penalize weights
  • Weight Decay
  • Weight Elimination
• Algorithms that update learning rate
  • Adaptive Learning Rate
  • Error difference Update
  • Linear search using Golden Search or Brent
  • Search than converge
  • Simple Step Minimization
• Ensembles
  • Mixture of Experts
  • Dynamically Averaged Network (DAN)
• Neural Networks with Radial Basis Functions (RBFN)
  • Generalized Regression Neural Network (GRNN)
  • Probabilistic Neural Network (PNN)
  • Radial basis function K-means
• Autoasociative Memory
  • Discrete BAM Network
  • CMAC Network
  • Discrete Hopfield Network
• Competitive Networks
  • Adaptive Resonance Theory (ART1) Network
  • Self-Organizing Feature Map (SOFM or SOM)
• Linear networks
  • Perceptron
  • LMS Network
  • Modified Relaxation Network
• Associative
  • OJA
  • Kohonen
  • Instar
  • Hebb