dnngraph

Deep network designer

A DSL and toolkit for designing and optimizing deep neural networks in Haskell

A DSL for deep neural networks, supporting Caffe and Torch

GitHub

700 stars
40 watching
59 forks
Language: Haskell
last commit: almost 9 years ago
Linked from 1 awesome list


Backlinks from these awesome lists:

Related projects:

Repository Description Stars
joeddav/devol An evolutionary algorithm for designing neural networks in Keras 950
ajtulloch/haskell-ml Implementations of basic machine learning algorithms in Haskell 57
huwcampbell/grenade A library for building complex neural networks in Haskell with a focus on dependently typed specifications and composability 1,449
jbarrow/lambdanet An artificial neural network library for rapid prototyping and extension in Haskell. 377
brunjlar/neural A Haskell-based framework for flexible neural networks and similar parameterized models with automatic differentiation and modular training algorithms. 123
hasktorch/hasktorch A Haskell library for building and training neural networks with native C++ libraries. 1,069
dmlc/gnnlens2 An interactive visualization tool for graph neural networks 239
unagiootoro/ruby-dnn A Ruby-based deep learning library for building and training neural networks 46
deepwisdom/autodl Automated deep learning algorithm that performs feature engineering, model selection, and hyperparameter tuning without human intervention. 1,140
namisan/mt-dnn A PyTorch package implementing multi-task deep neural networks for natural language understanding 2,238
doonny/pipecnn A tool for accelerating convolutional neural networks on Field-Programmable Gate Arrays (FPGAs) using OpenCL-based hardware design 1,253
dathoangnd/gonet A Go module implementing a multi-layer Neural Network for machine learning tasks 82
awslabs/dgl-lifesci A Python package for graph neural networks applied to life science domains 728
google-deepmind/jraph A lightweight library for working with graph neural networks in jax. 1,375
mosdeo/lkydeepnn A header-only C++ library for training and deploying deep neural networks on embedded systems 49