In the data-driven era, neural networks are transforming businesses, uplifting everyday life and bringing us to the next level AI.
Following the functionality of human brain cells, neural networks train and strengthen machines (such as smart mobiles or computers) to learn, recognize and make predictions as a human mind does and solve business problems in every domain.
“I’m not suggesting that neural networks are easy. You need to be an expert to make these things work. But that expertise serves you across a broader spectrum of applications. In a sense, all of the effort that previously went into feature design now goes into architecture design and loss function design and optimization scheme design. The manual labor has been raised to a higher level of abstraction.” ― Stefano Soatto
In this blog, we will talk about basic aspects of neural networks along with a core discussion over a few automated neural network software that are committed to deliver greater convenience in numerous ways, especially in everyday life.
In particular, an input layer, an output layer and a hidden layer sandwiched amid them, these layers are interconnected through nodes and together design a network- a system of neural networks of interconnected nodes.
Neural networks work similarly to the human brain’s neural network where a neuron in neural networks is a mathematical function that accumulates and categorizes insights with respect to a particular architecture. A neural network embraces a strong correspondence with statistical data models such as curve fitting and regression analysis.
As neural networks are a system of interconnected nodes, these nodes are perceptrons and similar to multiple linear regression models. In multi-layered perceptron model, perceptrons are structured in between interconnected layers,
the input layer to assemble input patterns,
the output layer to hold classifications or output signals that could map with input patterns.
the hidden layer to fine-tune input weights to meet the minimal margin error of neural networks.
The neural network software is used to research, create, imitate and apply artificial neural network and software concepts simulating the biological nervous system. These software are aimed for practical applications of ANN such as data mining and forecasting.
The neural network software is designed and developed by a number of software firms- Google Inc., Qualcomm Technologies, and Intel Corporation, among others. Neural networks software are getting popular due to their expanded range of applications and strength.
(Read also: Introduction to Neural Network and Deep Learning)
Following are the some selected neural network software;
Best neural network software
One of the professional applications, the neural designer is used in order to detect unseen patterns, convoluted relationships and to anticipate certain trends from datasets using neural networks.
Neural designer has become one of the most used desktop applications for data mining, basically neural designer employs neural networks as mathematical models imitating human brain functionality. It designs sufficient computational models functioning as the central nervous system.
Neural Designer is a code-free app for data science and machine learning that allows you to easily build AI powered applications.- Neural Designer
A deep learning library developed over TensorFlow, Tflearn is a modular and transparent library that was aimed to give a top-level API to TensorFlow while designing. It supports active experimentations and holds complete transparency and compatibility with TensorFlow. The current API supports multiple deep learning models including LSTM, PReLU, Generative Networks etc. Learn more about TensorFlow from the link.
Tflearn has the following feature;
Smooth device-installation to use various CPU/GPU
Precise and attractive graph visualization detailing weight, gradients, activations and many more.
Sufficient supportive functions in order to train TensorFlow graphs through many inputs, outputs and optimizers.
A high-level API which is easy to understand and implement deep neural networks with exemplary tutorials.
Quick and efficient prototyping with modular embedded neural networks layers, regularizers, optimizers, metrics, etc.
Clear transparency to TensorFlow such as each function is developed over tensors while can be deployed independently of Tflearn.
NeuroSolution is a neural network software development environment blending a modular, icon-based network design interface that employs advanced AI and machine learning algorithms such as Conjugate Gradients, Levenberg Marquardt, Back Propagation time etc.
As neural network software, NeuroSolution products are hugely deployed for data mining in order to make substantial predictive models through advanced processing techniques, automated neural networks topology search via cutting-edge distributed computing.
NeuroSolutions works with advanced learning procedures using easy excel interfaces or intuitive wizards. Moreover, the software gives additional wizards for building automated neural network models including Data Manager, Neural Building and Neural Expert.
In deep learning, the high-level neural network library, Keras, is composed in Python for TensorFlow and Theano with minimum functionalities and can execute on top of these applications. Keras is an API following best practices to reduce cognitive processing by rendering easy-to-use constant APIs such as it reduces the number of user actions necessary to process a task. Learn more about Keras from this tutorial.
In practice, Keras offers precise and actionable error detection as well as gives extensive documentation and developer guide. With active participation, it goes from idea to result without delaying.
As a deep learning library, the software enables fast and centralized prototyping via modularity and flexibility. Moreover, Keras supports convolutional neural networks (CNN), recurrent neural networks (RNN) and a combination of both. The default library for Keras is TensorFlow, even with its simple API, debugging of Keras Models becomes easier as they are built in Python.
The microsoft cognitive toolkit, or CNTK, is an commercially available open-source toolkit for deep learning systems. CNTK gives substantial scaling potential with speed and accuracy allowing users to extract information from massive datasets.
CNTK explains neural networks in the form of a computational process by a directed graph, Where the leaf nodes of a network graph can depict input values or network attributes.It enables users to couple popular model types such as DNN, CNN, RNN, or LSTM.
In general, the toolkit employs stochastic gradient descent learning with automatic differentiation and parallelization over various GPUs and servers. Some of Microsoft products such as Skype, Cortana, Bing, etc use this toolkit to build enterprise-level AI-based products.(Source)
The video below provides a high-level overview of toolkit;
ConvNetJS is a javascript library used to train deep learning models in users own browser, it just requires opening a tab and starting to train neural networks without the necessity of software, compilers, installations, GPUs and sweat. The code for libis available at GitHub under MIT license, the library is also available on npm for use in Nodejs by the name convnetjs.
The software enables users to design and solve neural network models in Javascript, originally transcripted by PhD students, Andrej Karpathy, from Stanford, but later on the library has extended through community’s contributions.
Currently, it supports;
Fully connected layers, non-linearities, in common neural network modules,
Classification and Regression problems such as SVM/Softmax and L2 regularization & cost functions respectively,
An experimental reinforcement learning module depending upon Deep Q Learning, and
To Describe and train convolutional neural networks to process images, etc. (from)
Torch is an open-source, scientific computing framework supporting machine learning algorithms through GPU. It deploys scripting language LuaJIT with an underlying C/CUDA implementation.
Torch provides a variety of efficiencies including N-dimensional array features, loads of routines for indexing, splitting and transposing to C via LuaJIT, and neural network models. The software facilitates impressive GPU support and can work with iOS, Android, etc.
Other features include neural network and energy based models, numerical optimization routine, etc. With the objective to magnify adaptability and agility in developing scientific algorithms, Torch makes the process very simple via facilitating a huge portrait of community-driven packages in machine learning, computer vision, signal processing and video & image processing etc.
With the emergence of neural networks, the concept is being broadly used for data analysis where neural networks simulation makes the analysis more faster with accurate predictions than other analysis methods.
For example, time series forecasting, function approximation, regression analysis, etc can be conducted with neural network software. Possible applications of neural networks are game forecasting, decision support, pattern recognition, automated control systems and many more, the method plays an important role across data mining processes and tools.
So at the end, we have known a few best neural networks software that can imitate human brain functionality to process data and recognize data patterns for easier and effective decision making.
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