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Top 30 Artificial Neural Network Software
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Top 30 Artificial Neural Network Software

Top 30 Artificial Neural Network Software
4.8 (96.19%) 21 ratings

Artificial Neural network software apply concepts adapted from biological neural networks, artificial intelligence and machine learning and is used to simulate, research, develop Artificial Neural network. Neural network simulators are software applications that are used to simulate the behavior of artificial or biological neural networks which focus on one or a limited number of specific types of neural networks. They are typically stand-alone and not intended to produce general neural networks that can be integrated in other software. Simulators usually have some form of built-in visualization to monitor the training process and some simulators also visualize the physical structure of the neural network. In order for neural network models to be shared by different applications, Predictive Model Markup Language (PMML) is used. PMML is an XML-based language which provides a way for applications to define and share neural network models and other data mining models between PMML compliant application.

Top Artificial Neural Network Software : Neural Designer, GMDH Shell, Neuroph, Darknet, DeepLearningKit, Tflearn, ConvNetJS, NeuroSolutions, Torch, Keras, NVIDIA DIGITS, Stuttgart Neural Network Simulator, DeepPy, MLPNeuralNet, Synaptic, DNNGraph, NeuralN, AForge.Neuro, NeuralTalk2, cuda-convnet2, Knet, DN2A, neon, HNN, Lasagne, gobrain, LambdaNet, RustNN, Mocha, deeplearn-rs are some of the Top Artificial Neural Network Software.

What are Artificial Neural Network Software?

Artificial Neural Network Software is used to simulate, research, develop, and apply artificial neural networks, software concepts adapted from biological neural networks.  Artificial Neural Network Software are intended for practical applications of artificial neural networks with the primary focus is on data mining and forecasting. These data analysis simulators usually have some form of preprocessing capabilities and use a relatively simple static neural network that can be configured.

Top Artificial Neural Network Software

Neural Designer, GMDH Shell, Neuroph, Darknet, DeepLearningKit, Tflearn, ConvNetJS, NeuroSolutions, Torch, Keras, NVIDIA DIGITS, Stuttgart Neural Network Simulator, DeepPy, MLPNeuralNet, Synaptic, DNNGraph, NeuralN, AForge.Neuro, NeuralTalk2, cuda-convnet2, Knet, DN2A, neon, HNN, Lasagne, gobrain, LambdaNet, RustNN, Mocha, deeplearn-rs are some of the Top Artificial Neural Network Software .

Top Artificial Neural Network Software
PAT Index™
 
Neural Viewer
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 

1

Neural Designer

Neural Designer is a desktop application for data mining which uses neural networks, a main paradigm of machine learning. The software is developed by the startup company called Artelnics, based in Spain and founded by Roberto Lopez and Ismael Santana. Neural networks are mathematical models of the brain function, computational models which are inspired by central nervous systems, in particular the brain, which can be trained to perform certain tasks. Neural networks are capable of machine learning as well as pattern recognition. Neural networks are generally presented as systems of interconnected neurons, which can compute outputs from inputs. Neural network…

Bottom Line

Neural Designer is a professional application for discovering complex relationships, recognizing unknown patterns and predicting actual trends from data sets by means of neural networks. Some of the examples where Neural Designer has used are in flight data to increase comfort and reduce consumption of aircrafts, in medical databases to make more reliable and less invasive diagnosis. Neural Designer has also used in physico-chemical data to increase the quality of wines and in sales data to optimize provisioning and to improve work quadrants.

9.5
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8.8
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Neural Designer

Neural Viewer

2

GMDH Shell

GMDH Shell is a Forecasting Software that optimizes company’s inventory levels. GMDH Shell aligns inventory with customer’s demand which can avoid shortages or excess stock, reduce inventory carrying costs and increase operating cash flow. Users are able to build an accurate forecast of demand and sales. It also matches supply to demand and build company’s sales plan which promotes generating higher profits.GMDH Shell builds multivariate linear regression models with seasonal, Fourier, and trend components. Users can take advantage of the fast multivariate tools with superior optimization of core algorithms and excellent parallel processing capabilities.It’s fast and intuitive user interface helps…

7.6
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8.1
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7 ratings
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GMDH Shell

3

Neuroph

Neuroph is lightweight Java neural network framework to 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. Also has nice GUI neural network editor to quickly create Java neural network components. It has been released as open source under the Apache 2.0 license. Neuroph simplifies the development of neural networks by providing Java neural network library and GUI tool that supports creating, training and saving neural networks. Neuroph is best for beginners with neural networks, especially if the user just wanted to try how…

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Neuroph simplifies the development of neural networks by providing Java neural network library and GUI tool that supports creating, training and saving neural networks.

7.6
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5.6
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9 ratings
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Neuroph

4

Darknet

Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. Users can find the source on GitHub. Darknet is installed with only two optional dependencies: OpenCV if users want a wider variety of supported image types or CUDA if they want GPU computation. Neither is compulsory but users can start by just installing the base system which has only been tested on Linux and Mac computers. The framework features You Only Look Once (YOLO), a state-of-the-art, real-time object detection system. On a Titan X it…

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Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.

7.7
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5.0
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4 ratings
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Darknet

5

DeepLearningKit

DeepLearningKit is an Open Source with Apache 2.0 License. It is a Deep Learning Framework for Apple’s iOS, OS X and tvOS that is available at github.com/DeepLearningKit/DeepLearningKit. The goal is to support using pre-trained Deep Learning models on all Apple’s devices that have GPU(s). It is developed in Swift to easily run on all platforms such as iOS, OS X and tvOS and Metal to efficiently use on-device GPU to ensure low-latency Deep Learning calculations.DeepLearningKit currently supports using (Deep) Convolutional Neural Networks, such as for image recognition, trained with the Caffe Deep Learning Framework but the long term goal is…

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DeepLearningKit currently supports using (Deep) Convolutional Neural Networks, such as for image recognition, trained with the Caffe Deep Learning Framework but the long term goal is to support using deep learning models trained with the most popular Deep Learning frameworks such as TensorFlow and Torch.

7.5
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5.2
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DeepLearningKit

6

Tflearn

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. The high-level API currently supports most of recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks... In the future, TFLearn is also intended to stay up-to-date with latest deep learning techniques and it is currently in its early development stage. TFLearn requires Tensorflow (version >= 0.9.0) to be installed. The easiest way to install TFLearn…

Bottom Line

TFLearn features include easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples and fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics.

7.7
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8.0
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Tflearn

7

ConvNetJS

ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in users’ browsers. Users just open a tab and they are training. No software requirements, no compilers, no installations, no GPUs, no sweat. The library allows users to formulate and solve Neural Networks in Javascript, and was originally written by @karpathy (a PhD student at Stanford). However, the library has since been extended by contributions from the community. The code is available on Github under MIT license. Pull requests for new features / layers / demos and miscellaneous improvements are encouraged. The library is also available on…

Bottom Line

The library allows you to formulate and solve Neural Networks in Javascript. Current support includes Common Neural Network modules (fully connected layers, non-linearities), Classification (SVM/Softmax) and Regression (L2) cost functions, Ability to specify and train Convolutional Networks that process images and An experimental Reinforcement Learning module, based on Deep Q Learning.

7.6
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5.6
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1 rating
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ConvNetJS

8

NeuroSolutions

NeuroSolutions is an easy-to-use neural network software package for Windows. It combines a modular, icon-based network design interface with an implementation of advanced artificial intelligence and learning algorithms using intuitive wizards or an easy-to-use Excel interface. NeuroSolutions is designed to make neural networks easy and accessible to both novice and advanced developers. There are three basic phases in neural network analysis: training the network on the data, testing the network for accuracy and making predictions/classifying from new data. The Express Builder in the NeuroSolutions Excel interface can accomplish all of this automatically in one simple step. With NeuroSolutions Excel interface,…

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The NeuroSolutions product family is leading-edge neural network software for data mining to create highly accurate and predictive models using advanced preprocessing techniques, intelligent automated neural network topology search through cutting-edge distributed computing.

7.6
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7.7
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1 rating
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NeuroSolutions

9

Torch

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It offers an easy to use and efficient program to its users, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. Users of Torch will be able to take advantage of its core features such as a powerful N-dimensional array, lots of routines for indexing, slicing, transposing, amazing interface to C, via LuaJIT, linear algebra routines, neural network, and energy-based models, numeric optimization routines, fast and efficient GPU support and embeddable, with ports to iOS, Android and FPGA…

Bottom Line

Torch is a popular neural network and optimization libraries which offers simple to use function for its users while having maximum flexibility in implementing complex neural network topologies. Users can build arbitrary graphs of neural networks and parallelize them over CPUs and GPUs in an efficient manner.

7.5
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7.7
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3 ratings
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Torch

10

Keras

Keras is a deep learning library for Theano and TensorFlow. It is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras deep learning library allows for easy and fast prototyping (through total modularity, minimalism, and extensibility). It supports both convolutional networks and recurrent networks, as well as combinations of the two. Keras also supports arbitrary connectivity schemes (including multi-input and…

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Keras allows for easy and fast prototyping (through total modularity, minimalism, and extensibility), supports both convolutional networks and recurrent networks, as well as combinations of the two and supports arbitrary connectivity schemes (including multi-input and multi-output training).

7.6
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9.2
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3 ratings
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Keras

11

NVIDIA DIGITS

The NVIDIA DIGITS puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks without the need to write code. NVIDIA DIGITS simplifies common deep learning tasks for its users such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real time with advanced visualizations and selecting the best performing model from the results browser for deployment. NVIDIA DIGITS is completely interactive so that data scientists can focus on designing…

Bottom Line

NVIDIA GPUs for deep learning are available in desktops, notebooks, servers, and supercomputers around the world, as well as in cloud services from Amazon, IBM, Microsoft, and Google. You can choose a plug-and-play deep learning solution powered by NVIDIA GPUs or build your own

7.5
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7.3
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2 ratings
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NVIDIA DIGITS

12

Stuttgart Neural Network Simulator

The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the 'RSNNS' low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R. The package provides a convenient interface to the popular Stuttgart Neural Network Simulator SNNS. It includes functions for visualization and analysis of the models and the training procedures, as well as…

Bottom Line

Using the 'RSNNS' low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.

7.5
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7.6
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Stuttgart Neural Network Simulator

13

DeepPy

DeepPy is a MIT licensed deep learning framework. DeepPy tries to add a touch of Zen to deep learning as it allows for Pythonic programming based on NumPy’s ndarray. It also has a small and easily extensible codebase while DeepPy runs on CPU or Nvidia GPUs. DeepPy Implements the following network architectures: Feedforward networks, Convents, Siamese networks and Autoencoders. However, note that DeepPy is by no means a mature project and should be considered WIP. DeepPy

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DeepPy allows for Pythonic programming based on NumPy’s ndarray. Has a small and easily extensible codebase. Runs on CPU or Nvidia GPUs (thanks to CUDArray) and implements the network architectures.

7.5
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7.6
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DeepPy

14

MLPNeuralNet

MLPNeuralNet is a fast multilayer perceptron neural network library for iOS and Mac OS X. MLPNeuralNet predicts new examples through trained neural networks. It is built on top of Apple's Accelerate Framework using vectored operations and hardware acceleration (if available). MLPNeuralNet is for users who have engineered a prediction model using Matlab (Python or R) and would like to use it in an iOS application. In that case, MLPNeuralNet is exactly what is needed. MLPNeuralNet is designed to load and run models in forward propagation mode only. Some of the features that users will be able to take advantage of…

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MLPNeuralNet is designed to load and run models in forward propagation mode only. Some of the features that users will be able to take advantage of MLPNeuralNet would be Classification, Multi-class classification and regression output, Vectorised implementation, Works with double precision and Multiple hidden layers or none (in that case it's same as logistic/linear regression).

7.5
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7.3
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1 rating
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MLPNeuralNet

15

Synaptic

Synaptic is a javascript neural network library for node.js and the browser, its generalized algorithm is architecture-free, so users can build and train basically any type of first order or even second order neural network architectures. This library includes a few built-in architectures like multilayer perceptrons, multilayer long-short term memory networks (LSTM), liquid state machines or Hopfield networks, and a trainer capable of training any given network, which includes built-in training tasks/tests like solving an XOR, completing a Distracted Sequence Recall task or an Embedded Reber Grammar test, so users can easily test and compare the performance of different architectures.…

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Networks can be imported/exported to JSON, converted to workers or standalone functions. They can be connected to other networks or gate connections.

7.5
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6.8
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2 ratings
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Synaptic

16

DNNGraph

DNNGraph is a deep neural network model generation DSL in Haskell. It is a DSL for specifying the model. This uses the lens library for elegant, composable constructions, and the fgl graph library for specifying the network layout. A set of optimization passes that run over the graph representation to improve the performance of the model. For example, users can take advantage of the fact that several layers types (ReLU, Dropout) can operate in-place. DNNGraph also offers a set of backends to generate code for the platform. Currently, DNNGraph generate Caffe (by generating model prototxt files) and Torch (by generating…

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DNNGraph optimization passes that run over the graph representation to improve the performance of the model. For example, we can take advantage of the fact that several layers types (ReLU, Dropout) can operate in-place.

7.5
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7.5
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1 rating
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DNNGraph

17

NeuralN

NeuralN is a C++ Neural Network library for Node.js with multiple advantages compared to existing solutions. It works with extra-large datasets (>1Go allowed by nodejs) and Multi-Threaded training available. With Node.js and the V8, it is not possible to work with large datasets since the maximum allowed memory is around 512MB for 32-bits machines and 1GB for 64-bits machines. When working with datasets of several gigabytes, it quickly becomes difficult to train the network with all the data. NeuralN allows users to use datasets as big as their memory can contain. Working with large datasets increases the performances of the…

Bottom Line

With Node.js and the V8, it is not possible to work with large datasets since the maximum allowed memory is around 512MB for 32-bits machines and 1GB for 64-bits machines.With the multi-threaded training method of NeuralN, you can significantly reduce the duration of the learning phase, by training your network simultaneously on different parts of your dataset.

7.5
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6.1
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1 rating
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NeuralN

18

AForge.Neuro

AForge.NET framework provides neural networks library, which contains set of classes aimed for creating different type of artificial neural networks and training them to solve certain tasks, like recognition, approximation, prediction, etc. The library mainly allows users to create two categories of artificial neural networks: feed forward neural networks with activation function and one layer distance networks. Feed forward neural networks are represented as one layer or as multi-layer networks which don't have recurrent connections. Information flows in these networks from inputs to outputs passing all layers of neural network only one time without doing loops. Neurons of such networks…

Bottom Line

AForge.NET framework consists of several libraries, so below the framework's features are presented grouped by them. AForge.Imaging, which is the biggest library of the framework so far, contains different image processing routines, which are aimed to help as in image enhancement/processing.

7.6
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7.7
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AForge.Neuro

19

NeuralTalk2

NeuralTalk2 is an efficient image captioning code in Torch that runs on GPU. Compared to the original NeuralTalk this implementation is batched, uses Torch, runs on a GPU, and supports CNN fine-tuning. All of these put together results in quite a large increase in training speed for the Language Model (~100 x). Image Captioning aims to describe photos with better accuracy. For users who just wanted to caption images, there would be a need to run the evaluation script on a pre-trained model checkpoint. This pre-trained checkpoint can be downloaded. Auto image captioning seems to be an impossible thing for…

Bottom Line

NeuralTalk2 is an early code release that works great but is slightly hastily released and probably requires some code reading of inline comments.

7.5
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7.5
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NeuralTalk2

20

cuda-convnet2

cuda-convnet2 is Nervana's fork of Alex Krizhevsky's cuda-convnet2 containing several extensions including: a new python backend called cudanet for integration into Nervana's neon framework. They have also included several new kernels and functions to support things like multiway costs, python interface to GPU memory, support for non-texture kernels, array and scalar max/min comparisons, and local contrast normalization. This version also features one line pip or cmake based installation and additional checking and fixes. To be able to install the framework users must ensure that they have met all required dependency packages including installing the CUDA toolkit and CUDA SDK. They…

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cuda-convnet2 is Nervana's fork of Alex Krizhevsky's cuda-convnet2 containing several extensions including: a new python backend called cudanet for integration into Nervana's neon framework.

7.5
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7.6
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cuda-convnet2

21

Knet

Knet is the Koç University deep learning framework that supports GPU operation and automatic differentiation using dynamic computational graphs for models defined in plain Julia. Knet uses dynamic computational graphs generated at runtime for automatic differentiation of (almost) any Julia code. This allows machine learning models to be implemented by defining just the forward calculation using the full power and expressivity of Julia. The implementation can use helper functions, loops, conditionals, recursion, closures, tuples and dictionaries, array indexing, concatenation and other high level language features, some of which are often missing in the restricted modeling languages of static computational graph…

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Knet uses dynamic computational graphs generated at runtime for automatic differentiation of (almost) any Julia code. This allows machine learning models to be implemented by defining just the forward calculation (i.e. the computation from parameters and data to loss) using the full power and expressivity of Julia

7.5
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2.9
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Knet

22

DN2A

DN2A is a set of highly decoupled JavaScript modules for Neural Networks and Artificial Intelligence development. Each module is based on injection by configuration. Clients can use a single module alone, more of them together or just the complete set. DN2A’s main goal is to allow users to design, train and use Single Neural Networks with ease as well as very powerful Neural Networks Chains through which to implement their Artificial Intelligence solutions. DN2A’s side goals are to simplify integration, to speed up training/querying, to allow clustering and to represent the architecture and the relative data of each Neural Network…

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DN2A main goal is to allow you to design, train and use without pain Single Neural Networks as well as very powerful Neural Networks Chains through which implement your Artificial Intelligence solution.

7.5
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7.2
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1 rating
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DN2A

23

neon

Neon is Nervana’s Python-based deep learning library. It provides ease of use while delivering the highest performance. Some of the features that neon has would be the support for commonly used models including convnets, RNNs, LSTMs, and autoencoders, tight integration with neon’s state-of-the-art GPU kernel library, 3s/macrobatch (3072 images) on AlexNet on Titan X (Full run on 1 GPU ~ 32 hrs), basic automatic differentiation support, framework for visualization and swappable hardware backends: write code once and deploy on CPUs, GPUs, or Nervana hardware. Neon supports loading of both common and custom datasets. Data should be loaded as a python…

Bottom Line

neon support for commonly used models including convnets, RNNs, LSTMs, and autoencoders and can find many pre-trained implementations of these in our model zoo and tight integration with our state-of-the-art GPU kernel library.

7.5
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6.4
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neon

24

HNN

HNN stands for Haskell Neural Network library; it is an attempt at providing a simple but powerful and efficient library to deal with feed-forward neural networks in Haskell. It is a neural network library implemented purely in Haskell, relying on the hmatrix library. This library provides a straight and simple feed-forward neural networks implementation which is way better than the one in hnn-0.1, in all aspects.Haskell is an advanced purely-functional programming language. An open-source product of more than twenty years of cutting-edge research, it allows rapid development of robust, concise, correct software. With strong support for integration with other languages,…

7.5
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6.4
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HNN

25

Lasagne

Lasagne is a lightweight library to build and train neural networks in Theano. It supports feed-forward networks such as Convolutional Neural Networks (CNNs), recurrent networks including Long Short-Term Memory (LSTM), and any combination thereof. Lasagne allows architectures of multiple inputs and multiple outputs, including auxiliary classifiers. It also offers many optimization methods including Nesterov momentum, RMSprop and ADAM. Users can get a freely definable cost function and no need to derive gradients due to Theano's symbolic differentiation. It provides transparent support of CPUs and GPUs due to Theano's expression compiler. Lasagne grew out of a need to combine the flexibility…

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Lasagne main features supports feed-forward networks such as Convolutional Neural Networks (CNNs), recurrent networks including Long Short-Term Memory (LSTM), and any combination thereof and allows architectures of multiple inputs and multiple outputs, including auxiliary classifiers.

7.5
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6.7
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Lasagne

26

gobrain

The version 1.0.0 of gobrain includes just basic Neural Network functions such as Feed Forward and Elman Recurrent Neural Network. Using the framework, users are able to construct a simple Feed Forward Neural Network by first creating the XOR representation pattern to train the network. The networks structure should contain a specific number of inputs, hidden nodes and outputs. The training should run for a given number of epochs e.g. 1000. The learning rate can be set to 0.6 and the momentum factor to 0.4. Users can receive reports about the learning error by using true in the last parameter.…

Bottom Line

Using the framework, users are able to construct a simple Feed Forward Neural Network by first creating the XOR representation pattern to train the network. The networks structure should contain a specific number of inputs, hidden nodes and outputs.

7.5
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6.3
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gobrain

27

LambdaNet

LambdaNet is an artificial neural network library written in Haskell that abstracts network creation, training, and use as higher order functions. The benefit of this approach is that it provides a framework in which users can: quickly iterate through network designs by using different functional components as well as experiment by writing small functional components to extend the library. It comes with a pre-defined set of functions that can be composed in many ways to operate on real-world data. LambdaNet can be installed through Cabal and using it to rapidly prototype networks using built-in functions requires only a minimal level…

Bottom Line

LambdaNet provides a framework in which users can quickly iterate through network designs by using different functional components, and experiment by writing small functional components to extend the library.The library comes with a pre-defined set of functions that can be composed in many ways to operate on real-world data. These will be enumerated later in the documentation.

7.5
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6.6
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LambdaNet

28

RustNN

RustNN is a feedforward neural network library. The library generates fully connected multi-layer artificial neural networks that are trained via backpropagation. Networks are trained using an incremental training mode. RustNN can create a neural network with a given number of nodes in the input layer, the hidden layer and the output layer. The network is trained on tuples of vectors where the first vector is the inputs and the second vector is the expected outputs. Users can create a new neural network by passing a pointer to an array that specifies the number of layers and the number of nodes…

Bottom Line

RustNN is a feedforward neural network library. The library generates fully connected multi-layer artificial neural networks that are trained via backpropagation. Networks are trained using an incremental training mode. RustNN can create a neural network with a given number of nodes in the input layer, the hidden layer and the output layer.

7.5
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6.4
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RustNN

29

Mocha

Mocha is a Deep Learning framework for Julia, inspired by the C++ framework Caffe. Efficient implementations of general stochastic gradient solvers and common layers in Mocha could be used to train deep / shallow (convolutional) neural networks, with (optional) unsupervised pre-training via (stacked) auto-encoders. Mocha has a clean architecture with isolated components like network layers, activation functions, solvers, regularizers, initializers, etc. Built-in components are sufficient for typical deep (convolutional) neural network applications and more are being added in each release. All of them could be easily extended by adding custom sub-types. Mocha is written in Julia, a high-level dynamic programming…

Bottom Line

Mocha has a clean architecture with isolated components like network layers, activation functions, solvers, regularizers, initializers, etc. Built-in components are sufficient for typical deep (convolutional) neural network applications and more are being added in each release

7.5
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6.5
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Mocha

30

deeplearn-rs

Deeplearn-rs is a deep neural network in Rust. It has currently implemented models such as MNIST handwritten digit recognition and char-rnn using LSTM. Layers that has already been implemented are Matrix multiply (fully connected), Add (for bias, for example), LSTM, Softmax, MSE loss and Cross entropy loss. deeplearn-rs have SGD and RMSProp as optimizers.Deeplearn-rs provide utilities for working with data which includes images, TSV and CSV and raw text data and word embeddings. It also allows data types other than f32 and implements casting between arrays of primitive numeric types as well as more layer types such as Conv2d, Pooling…

Bottom Line

deeplearn-rs have SGD and RMSProp as optimizers.Deeplearn-rs provide utilities for working with data which includes images, TSV and CSV and raw text data and word embeddings.

7.5
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5.4
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deeplearn-rs

Top Artificial Neural Network Software at a Glance

Artificial Neural Network Software: ALL
PAT Index™
 
1
Neural Designer
 
2
Neuroph
 
3
Darknet
 
4
DeepLearningKit
 
5
Keras
 
6
Tflearn
 
7
NeuroSolutions
 
8
ConvNetJS
 
9
Torch
 
10
Stuttgart Neural Network Simulator
 
11
NVIDIA DIGITS
 
12
DeepPy
 
13
Synaptic
 
14
MLPNeuralNet
 
15
DNNGraph
 
16
AForge.Neuro
 
17
cuda-convnet2
 
18
NeuralN
 
19
NeuralTalk2
 
20
Knet
 
21
HNN
 
22
Lasagne
 
23
neon
 
24
LambdaNet
 
25
gobrain
 
26
DN2A
 
27
RustNN
 
28
deeplearn-rs
 
29
Mocha
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