ANNs are lone performers and not intended to produce general neural networks

The concept of neural networks is widely used for data analysis nowadays. An Artificial Neural Network (ANN) is a piece of computing system designed to simulate the way the human brain analyses and processes information. Ultimately, neural network software is used to simulate, research, develop and apply ANN, software concept adapted from biological neural networks. In some cases, a wider array of adaptive systems such as artificial intelligence and machine learning are also benefited.

ANNs are lone performers and not intended to produce general neural networks that can be integrated into other software. ANN software is for practical applications of artificial neural networks with a primary focus on data mining and forecasting. Data analysis simulators have some form of preprocessing capabilities and use a relatively simple static neural network that can be configured.

Here are some top Artificial Neural Network Software to look out for

Neural Designer

Neural Designer software is developed by the startup company called Artelnics, headquartered in Spain. The company was founded by Roberto Lopez and Ismael Santana.

Neural Designer is a desktop application for data mining that uses neural networks which is a paradigm of machine learning. Neural networks in Neural Designs are mathematical models of the brain functions, computational models which are inspired by central nervous systems in the brain that can be trained to perform certain tasks. Neural Designer has most of the advanced techniques for data preparations, machine learning and model deployment. Its visual graphical user interface provides comprehensive and visual results without the need to write code or assemble blocks. The software implements multicore processing to analyse larger amounts of data in less time.

Neuroph

Neuroph is an open-source project hosted at SourceForge under the Apache License. It is a library for creating neural networks and utilizing machine learning.

Neuroph is a lightweight Java neural network framework to develop common neural network architectures. Users can interact with Neuroph using,

• A GUI-based tool

• A Java library

Both approaches rely on an underlying class hierarchy which builds artificial neural networks out of layers of neurons.

Neuroph contains has nice GUI neural network editor to quickly create Java neural network components. The software simplifies the development of a neural network by providing Java neural network library and GUI tool that supports creating, training and saving neural networks.

Darknet

Darknet is an open-source neural network framework written in C and CUDA and supports CPU and GPU computation. It is a convolutional neural network that is nineteen layers deep. The pretrained network can classify images into 1000 object categories such as keyboard, mouse, pencil and many animals. As a result, the network has learned rich feature representation for a wide range of images.

Darknet is installed with only two optional dependencies like OpenCV if users want a wider variety of support image types or CUDA if they want GPU computation. The users can start by just installing the base system which has only been tested on Linux and Mac computers.

Keras

Keras is a deep learning library for Theano and TensorFlow. The high-level neural networks library is written in Python and capable of running on the top of both applications.

Keras is an API designed for human beings, not machines. The software follows best practices for reducing cognitive load. It offers consistent and simple APIs and minimizes the number of user actions required for common use cases. Keras provides clear and actionable error messages and has extensive documentation and developer guides. Keras deep learning library allows easy and fast prototyping through total modularity, minimalism, and extensibility. It supports convolutional neural networks and recurrent networks, as well as combinations of the two.

NeuroSolutions

NeuroSolutions is a neural network software development environment designed by NeuroDimension. It combines a modular, icon-based network design interface with an implementation of advanced learning procedures, such as conjugate gradients, Levenberg Marquardt and backpropagation through time.

NeuroSolutions product family is leading-edge neural network software for data mining to create highly accurate and predictive models using advanced processing techniques, intelligent automated neural network topology search through cutting-edge distributed computing. It is a design interface with advanced artificial intelligence and learning algorithms using intuitive wizards or an easy-to-use Excel interface. The software provides three separate wizards for automatically building neural network models,

• Data Manager

• Neural Builder

• Neural Expert

Tflearn

Tflearn is a modular and transparent deep learning library built on top of Tensorflow. The software is 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 and generative networks. Looking ahead, Tflearn is also intended to stay up-to-date with the latest deep learning techniques and it is currently in its early development stage.

ConvNetJS

ConvNetJS library allows users to formulate and solve neural networks in Javascript. It was originally written by a PhD student from Stanford. The library has since been extended by contributions from the community.

Users feel at ease as the network trains just by opening it without software requirements, compilers, installations, GPU and sweat. The code is available on Github under MIT license.

Torch

Torch is a scientific computing framework with wide support of machine learning algorithms that put GPUs first. Easy and fast scripting language LuaJIT and an underlying C'CUDA implementation offers an easy to use and efficient program to its users.

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, transporting, amazing interface to C, via LuaJIT, linear algebra routines, neural networks and energy-based models.

NVIDIA DIGITS

The NVIDIA Deep Learning GPU Training System (DIGITS) put the power of deep learning into the hands of engineers and data scientists. The software application uses highly accurate deep neural networks for image classification, segmentation and object detection tasks rapidly.

DIGITS simplifies common deep learning tasks 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. It is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging.

Stuttgart Neural Network Simulator

Stuttgart Neural Network Simulator (SNNS) is a neural simulator originally developed at the University of Stuttgart. It was initially built for X11 under Unix, later by JavaNNS.

The SNNS simulator consists of two main components,

• Simulator kernel is written in C

• Graphical user interface under X11R4 or X11R5

The goal of the SNNS project is to create an efficient and flexible simulation environment for research on and application of neural nets. The simulator kernel operates on the internal network data structures of the neural nets and performs all operations of learning and recall. It can also be used without the other parts as a C program embedded in custom applications. It supports arbitrary network topologies and, like RCS, supports the concept of sites. SNNS can be extended by the user with user-defined activation functions, output functions, site functions and learning procedures, which are written as simple C programs and linked to the simulator kernel.