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.
- 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.
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