Neuroph is lightweight Java neural network framework that develop common neural network architectures, it contains well designed, open source Java library with small number of basic classes which correspond to basic NN concepts and has s GUI neural network editor to quickly create Java neural network components.

Work for Neuroph?

Learning about Neuroph?

We can help you find the solution that fits you best.

Neuroph Reviews

Chat with a G2 Advisor
Write a Review
Filter Reviews
Filter Reviews
Company Size
Showing 1 Neuroph review
LinkedIn Connections
Neuroph review by Jhoneis M.
Jhoneis M.
Validated Reviewer
Review Source

"Neuroph "

What do you like best?

provides an additional graphical interface that allows you to create neural networks and is compatible with many neural networks such as kohonen

What do you dislike?

you must have advanced virtualization features, good processing since the demands are high, plus java is strictly required

Recommendations to others considering the product

First, research about the world of neuronal programming before making any investment, the objectives of use must be clear to know the scope of the product

What business problems are you solving with the product? What benefits have you realized?

we use it for neuronal development and web integration, we also take advantage of its apache and java environments to integrate with other platforms of the factory

Sign in to G2 to see what your connections have to say about Neuroph

What Artificial Neural Network solution do you use?

Thanks for letting us know!

There are not enough reviews of Neuroph for G2 to provide buying insight. Below are some alternatives with more reviews:

Mocha Logo
Mocha is a Deep Learning framework for Julia, inspired by the C++ framework Caffe that is efficient implementations of general stochastic gradient solvers and common layers, it could be used to train deep / shallow (convolutional) neural networks, with (optional) unsupervised pre-training via (stacked) auto-encoders.
Keras Logo
Keras is a neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
AWS Deep Learning AMIs Logo
AWS Deep Learning AMIs
The AWS Deep Learning AMIs is designed to equip data scientists, machine learning practitioners, and research scientists with the infrastructure and tools to accelerate work in deep learning, in the cloud, at any scale.
TFLearn Logo
TFlearn is a modular and transparent deep learning library built on top of Tensorflow that provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it.
ConvNetJS Logo
ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in a browser.
brain Logo
brain is a JavaScript neural network library to recognize color contrast.
julia-ann Logo
julia-ann is the implementation of backpropagation artificial neural networks in Julia that allow users to build multilayer networks and accept DataFrames as inputs. fit! and predict currently require Float64 matrices and vectors.
Deep Learning GPU Training System (DIGITS) Logo
Deep Learning GPU Training System (DIGITS)
NVIDIA Deep Learning GPU Training System (DIGITS) deep learning for data science and research to quickly design deep neural network (DNN) for image classification and object detection tasks using real-time network behavior visualization.
Torch Logo
Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first.
Knet Logo
Knet (pronounced "kay-net") is a deep learning framework implemented in Julia that allows the definition and training of machine learning models using the full power and expressivity of Julia.
Show more
Kate from G2

Learning about Neuroph?

I can help.
* We monitor all Neuroph reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. Validated reviews require the user to submit a screenshot of the product containing their user ID, in order to verify a user is an actual user of the product.