Deep Learning

NeuPy – Python library for Artificial Neural Networks – Deep Learning

NeuPy is an open source Python library for Artificial Neural Networks and Deep Learning.

NeuPy supports many different types of Neural Networks from a simple perceptron to deep learning models.

NeuPy is based on the Theano framework. This allows users to easily train neural networks with constructible architectures on GPU.

Features include:

  • Deep Learning.
  • Reinforcement Learning (RL).
  • Convolutional Neural Networks (CNN).
  • Recurrent Neural Networks (RNN).
  • Restricted Boltzmann Machine (RBM).
  • Multilayer Perceptron (MLP).
  • Networks based on the Radial Basis Functions (RBFN).
  • Associative and Autoasociative Memory.
  • Ensemble Networks.
  • Competitive Networks.
  • Basic Linear Networks.
  • Regularization Algorithms.
  • Step Update Algorithms.
  • Supports lots of different training algorithms based on the backpropagation:
    • Classic Gradient Descent – an optimization algorithm often used for finding the weights or coefficients of machine learning algorithms, such as artificial neural networks and logistic regression.
    • Mini-batch Gradient Descent – a variation of the gradient descent algorithm that splits the training dataset into small batches that are used to calculate model error and update model coefficients.
    • Conjugate Gradient – solve a symmetric positive-definite system of linear equations of dimension N in exactly N steps.
    • quasi-Newton – find zeroes or local maxima and minima of functions, as an alternative to Newton’s method.
    • Levenberg-Marquardt – solves generic curve-fitting problems.
    • Hessian – a matrix of all possible calculus second derivatives for a function.
    • Hessian diagonal.
    • Momentum – a method that helps accelerate SGD in the relevant direction and dampens oscillations.
    • RPROP – resilient backpropagation, a learning heuristic for supervised learning in feedforward artificial neural networks. It’s widely regarded as one of the best performing first-order learning methods for neural networks with arbitrary topology.
    • iRPROP+ – a modification of RPROP with a weight-backtracking scheme.
    • Quickprop – an implementation of the error backpropagation algorithm.
    • Adadelta – an extension of Adagrad that seeks to reduce its aggressive, monotonically decreasing learning rate.
    • Adagrad – an algorithm for gradient-based optimization. It’s useful for dealing with sparse data.
    • RMSProp – an adaptive learning rate method proposed by Geoff Hinton.
    • Adam – Adaptive Moment Estimation, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments.
    • AdaMax – a special case of Adam.

Support: Documentation, Cheat Sheet, GitHub code repository
Developer: Yurii Shevchuk
License: MIT License


Neupy is written in Python. Learn Python with our recommended free books and free tutorials.

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