R-universe search: topic:neural-networks

Showing 7 of total 7 results (show query)

bflammers

ANN2:Artificial Neural Networks for Anomaly Detection

Training of neural networks for classification and regression tasks using mini-batch gradient descent. Special features include a function for training autoencoders, which can be used to detect anomalies, and some related plotting functions. Multiple activation functions are supported, including tanh, relu, step and ramp. For the use of the step and ramp activation functions in detecting anomalies using autoencoders, see Hawkins et al. (2002) <doi:10.1007/3-540-46145-0_17>. Furthermore, several loss functions are supported, including robust ones such as Huber and pseudo-Huber loss, as well as L1 and L2 regularization. The possible options for optimization algorithms are RMSprop, Adam and SGD with momentum. The package contains a vectorized C++ implementation that facilitates fast training through mini-batch learning.

Maintained by Bart Lammers. Last updated 4 years ago.

anomaly-detection artificial-neural-networks autoencoders neural-networks robust-statistics openblas cpp openmp

13 stars 5.59 score 60 scripts

eagerai

tfaddons:Interface to 'TensorFlow SIG Addons'

'TensorFlow SIG Addons' <https://www.tensorflow.org/addons> is a repository of community contributions that conform to well-established API patterns, but implement new functionality not available in core 'TensorFlow'. 'TensorFlow' natively supports a large number of operators, layers, metrics, losses, optimizers, and more. However, in a fast moving field like Machine Learning, there are many interesting new developments that cannot be integrated into core 'TensorFlow' (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the community).

Maintained by Turgut Abdullayev. Last updated 3 years ago.

deep-learning keras neural-networks tensorflow tensorflow-addons tfa

20 stars 5.20 score 16 scripts

fchamroukhi

meteorits:Mixture-of-Experts Modeling for Complex Non-Normal Distributions

Provides a unified mixture-of-experts (ME) modeling and estimation framework with several original and flexible ME models to model, cluster and classify heterogeneous data in many complex situations where the data are distributed according to non-normal, possibly skewed distributions, and when they might be corrupted by atypical observations. Mixtures-of-Experts models for complex and non-normal distributions ('meteorits') are originally introduced and written in 'Matlab' by Faicel Chamroukhi. The references are mainly the following ones. The references are mainly the following ones. Chamroukhi F., Same A., Govaert, G. and Aknin P. (2009) <doi:10.1016/j.neunet.2009.06.040>. Chamroukhi F. (2010) <https://chamroukhi.com/FChamroukhi-PhD.pdf>. Chamroukhi F. (2015) <arXiv:1506.06707>. Chamroukhi F. (2015) <https://chamroukhi.com/FChamroukhi-HDR.pdf>. Chamroukhi F. (2016) <doi:10.1109/IJCNN.2016.7727580>. Chamroukhi F. (2016) <doi:10.1016/j.neunet.2016.03.002>. Chamroukhi F. (2017) <doi:10.1016/j.neucom.2017.05.044>.

Maintained by Florian Lecocq. Last updated 5 years ago.

artificial-intelligence clustering em-algorithm mixture-of-experts neural-networks non-linear-regression prediction robust-learning skew-normal skew-t skewed-data statistical-inference statistical-learning t-distribution unsupervised-learning openblas cpp

3 stars 5.12 score 11 scripts

techtonique

bcn:Boosted Configuration Networks

Boosted Configuration (neural) Networks for supervised learning.

Maintained by T. Moudiki. Last updated 6 months ago.

machine-learning neural-networks statistical-learning cpp

5 stars 4.00 score 4 scripts

okgreece

TrafficBDE:Traffic Predictions Using Neural Networks

Estimate and return either the traffic speed or the car entries in the city of Thessaloniki using historical traffic data. It's used in transport pilot of the 'BigDataEurope' project. There are functions for processing these data, training a neural network, select the most appropriate model and predict the traffic speed or the car entries for a selected time date.

Maintained by Kleanthis Koupidis. Last updated 3 years ago.

big-data-europe neural-networks thessaloniki traffic-status-prediction

1 stars 3.98 score 19 scripts

blmayer

deep:A Neural Networks Framework

This package provides a layer oriented way of creating neural networks, the framework is intended to give the user total control of the internals of a net without much effort. Use classes like PerceptronLayer to create a layer of percetron neurons, and specify how many you want. The package does all the tricky stuff internally leaving you focused in what you want. I wrote this package during a neural networks course to help me with the problem set.

Maintained by Brian. Last updated 5 years ago.

machine-learning neural-networks

6 stars 3.95 score 2 scripts

techtonique

nnetsauce:Randomized and Quasi-Randomized networks for Statistical/Machine Learning

Randomized and Quasi-Randomized networks for Statistical/Machine Learning

Maintained by T. Moudiki. Last updated 7 months ago.

deep-learning machine-learning neural-networks randomized-algorithms statistical-learning

2 stars 2.60 score 6 scripts