Displaying 1 to 6 from 6 results

generative-models - Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

  •    Python

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow. Also present here are RBM and Helmholtz Machine. Generated samples will be stored in GAN/{gan_model}/out (or VAE/{vae_model}/out, etc) directory during training.

dll - Deep Learning Library (DLL) for C++ (ANNs, CNNs, RBMs, DBNs...)

  •    C++

DLL is a library that aims to provide a C++ implementation of Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) and their convolution versions as well. It also has support for some more standard neural networks. Note: When you clone the library, you need to clone the sub modules as well, using the --recursive option.

netket - Machine learning algorithms for many-body quantum systems

  •    Python

NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and machine learning techniques. It is a Python library built on JAX. Netket supports MacOS and Linux. We reccomend to install NetKet using pip For instructions on how to install the latest stable/beta release of NetKet see the Getting Started section of our website.

xRBM - Implementation of Restricted Boltzmann Machine (RBM) and its variants in Tensorflow

  •    Python

For any questions, feedback, and bug reports, please use the Github Issues. This code is available under the MIT license.

jlearn - Machine Learning Library, written in J

  •    J

WIP Machine learning library, written in J. Various algorithm implementations, including MLPClassifiers, MLPRegressors, Mixture Models, K-Means, KNN, RBF-Network, Self-organizing Maps. Models can be serialized to text files, with a mixture of text and binary packing. The size of the serialized file depends on the size of the model, but will probably range from 10 MB and upwards for NN models (including convnets and rec-nets).

learnergy - 💡 Learnergy is a Python library for energy-based machine learning models.

  •    Python

Did you ever reach a bottleneck in your computational experiments? Are you tired of implementing your own techniques? If yes, Learnergy is the real deal! This package provides an easy-to-go implementation of energy-based machine learning algorithms. From datasets to fully-customizable models, from internal functions to external communications, we will foster all research related to energy-based machine learning. Read the docs at learnergy.readthedocs.io.