There is no question that neural network software can bring your business the additional benefits of making an accurate decision backed by data. Neural network software promises an improved decision-making process and many more benefits for your business.
However, your success depends on choosing the appropriate software for your business needs.
To help you pick a neural network software suitable for your needs, we’ve compiled a list of the 9 best neural network software available in 2022, along with the pros and cons of each.
Top 9 Best Neural Network Software
| Brand | Starting price | Best for |
|---|---|---|
| 1. Neural Designer | $2,495 – $7,495/year | Data mining |
| 2. Microsoft Cognitive Toolkit | Free | Users on a budget |
| 3. Keras | Free | Speed |
| 4. Clarifai | $30 per month | Non-code operators |
| 5. Supervisely | Free | Training data |
| 6. TensorFlow | Free | Learners |
| 7. Neuroph | Request quote | Common neural network architectures |
| 8. NVDIA DIGITS | Free | Data scientists focusing on design |
| 9. MLPNeuralNet | Free | IOS and Mac OS X |
#1. Neural Designer — Best for data mining.
Pricing: It includes a 15-day free trial. The packages include small, medium, and large. The packages are further divided into two categories — annual and lifetime subscriptions. The Small package is $2,495/year (per user), the Medium package is $4,995/year (per user), the Large package is $7,495/ year (per user). The lifetime subscription option for the small package is $6,245(per user), the medium package is $12,495(per user), and the large package is $18,745 (per user).
Neural Designer, created by Artelnics, is a code-free app for data science and machine learning that allows you to easily build AI-powered applications. It promises greater accuracy, faster training to boost your productivity, and a higher capacity to manage large data sets. Their team of scientists and engineers has more than 15 years of experience developing the most advanced technology and carrying out projects with large corporations. These trusted professionals can be trusted for your business needs.
| Pros | Cons |
| It contains a simple, effective, and friendly user design and interface. | The pricing structure can be significantly complex for users interested in purchasing a subscription. |
| It is easy to use and technically complete for beginners and advanced users. | The pricing for the software can be expensive. |
| Availability for Cloud is a big plus. | There are no integrations listed. |
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#2. Microsoft Cognitive Toolkit — Best for users on a budget
Pricing: Free
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. CNTK allows users to realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs/LSTMs). It is entirely free and useful for your business needs.
| Pros | Cons |
| No need to purchase expensive subscriptions since CNTK is free. | Lack of flexibility with the features contained in the software. |
| The interface is friendly for beginners. | Lack of customization with the features contained in the software. |
| Offers API integration | It can be buggy when updating. This can risk breaking production code. |
| A strong community is available for support when facing challenges and issues. | |
| Easy to use for beginners and advanced users. |
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#3. Keras — Best for speed
Pricing: Free.
Keras is a deep learning API written in Python. It was developed with a focus on enabling fast experimentation. By following the best practices of reducing cognitive overload Keras can offer consistent and simple APIs.
| Pros | Cons |
| Easy to use for beginners and advanced users | It contains limited features for data processing. |
| It allows for codes to execute efficiently. | It is not advanced and debuggable compared to other neural network software such as TensorFlow or PyTorch. |
| Simple, effective, and friendly user design. | It can be hard to customize models that have been built by someone else. |
| It allows for support with RNN and CNN. |
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#4. Clarifai — Best for non-code operators
Pricing: The packages are separated into four categories — Community, Essential, Professional, and Enterprise. The Community package is free and is for personal and academic projects. The Essential package starts at $30 per month. The Professional package starts at $30 per month. For the Enterprise package, you have to contact Clarifai and get a quote from them.
Clarifai was founded in 2013 by Matthew Zeiler and the company has now become a market leader. Clarifai offers AI-powered software solutions and supports the full AI development lifecycle including dataset preparation and model training and deployment. The specialty of this startup is in its deep learning models that are used to understand unstructured image, video, text, and audio data.
| Pros | Cons |
| Easy to use for the general audience. | It includes limited features that are available only for images. |
| It offers services to a wide range of industries such as the public sector, travel and tourism, retail, media and entertainment, insurance, E-commerce, digital asset management, data labeling, and aviation. | The customer service and support can be enhanced for a better customer experience. |
| Simple, friendly, and effective user design and interface. | They need more introductory information and guides for beginner users. |
| Not much of an online community and forum for those who are facing challenges online. | |
| Pricing structure can be complex and unclear |
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#5. Supervisely — Best for training data
Pricing: There are three packages offered by Supervisely — the Community package, the Business package, and the Enterprise package. The Community package is $0 per month is for data scientists and students. The Business package is coming soon and there is no current price for this package. The Enterprise package requires you to contact them, and they will give you a personalized quote suited for you. Each subscription is only for one user.
Supervisely developed as an internal tool for Deep Systems and was used in day-to-day work. The goal of Supervisely is to give AI companies the training data that they need. Supervisely is trusted by 50,000 companies worldwide and has garnered clients from multinational corporations such as Mazda, Alibaba, Cyient, Thornton Tomasetti, Huk-Coburg, Eurovia Vinci, Resson, Engie, and many more
| Pros | Cons |
| It offers a wide range of features that are also customizable. | It needs more detailed information for beginner users. |
| It can be used for project management. | |
| Simple, friendly, and effective user design and interface. | |
| The free package offers a wide range of features. | |
| Simple and transparent pricing structure. |
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#6. TensorFlow — Best for learners
Pricing: Free.
TensorFlow is an end-to-end platform that makes it easy for you to build and deploy ML models. It helps you solve challenging, real-world problems with machine learning. Many multinational companies use TensorFlow such as Airbnb, Coca-Cola, DeepMind, GE Healthcare, Google, Intel, and Twitter.
| Pros | Cons |
| Simple, friendly, and effective user design and interface. | It has limited bindings to only with Python. |
| It is easy to use for beginners and advanced users. | An error message on TensorFlow can be difficult to understand. |
| It provides resources to learn more in-depth about machine learning, responsible AI, and models and datasets. | The learning curve to use TensorFlow is steep. |
| It contains an active community that can be useful for those facing challenges and issues. | The debugging time is far too long which needs to be reduced to an appropriate time. |
| It is offered for free. | |
| It provides a comprehensive guide on how-to-use TensorFlow and understands the software’s ecosystem. |
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#7. Neuroph – Best for common neural network architectures.
Pricing: Contact Neuroph for pricing.
Neuroph is a lightweight Java neural network framework that is well designed and contains an open-source Java library. Neuroph simplifies the development of neural networks and is perfect for beginners.
| Pros | Cons |
| Small, intuitive, and easy to learn and use. | Website UX/UI design could be better. |
| It is easy to use for beginners and advanced users. | |
| Simple, friendly, and effective user design and interface. | |
| Easy to follow structure |
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#8. NVDIA DIGITS – Best for data scientists focusing on design.
Pricing: Free.
DIGITS simplifies common deep learning tasks for its users. These tasks include managing data, designing and training neural networks on multi-GPU systems, and monitoring performance with data advanced visualizations. DIGITS is used to train a highly accurate deep neural network (DNNs).
| Pros | Cons |
| It is free. It contains several features such as monitoring performance in real time and advanced visualizations. | Not beginner-friendly. |
| Available for desktops, notebooks, servers, and supercomputers around the world. | |
| Available for cloud services from Amazon, IBM, Microsoft, and Google. | |
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#9. MLPNeuralNet – Best for Mac OS X
Pricing: Free.
MLPNeuralNet is a multilayer neural network library for iOS and Mac OS X. It is an open-source program that works with double precision and contains features such as vectorized implementation, multi-class classification, and regression output.
| Pros | Cons |
| It is free. | Not beginner-friendly. |
| Available on iOS and Mac OS X. | Not available on Windows. |
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Frequently Asked Questions
What is a neural network?
A neural network reflects the behavior of human brains. The name is inspired by the way biological neurons signal to one another. A neural network allows computer programs to recognize patterns to solve real-world problems. It is a subset of machine learning and provides deep learning algorithms.
What is the anatomy of a neural network?
A neural network is a type of model which can be trained to recognize patterns. A neural network contains input, output, and hidden layers. Neurons are contained in each layer and can learn abstract representations of the data. For example, if you were to display an unlabeled input image the neuron will detect lines, shapes, and textures which makes it possible to classify what the image is.
How is machine learning different than computer programming?
Machine learning is the practice of commanding software to perform a specific task without explicit rules. Machine Learning is different than traditional computer programming where a programmer provides rules for the computer to use. Machine learning focuses more on data analysis rather than coding.
Is there a difference between neural networks and deep learning?
Although both terms are used interchangeably in conversation, there is a difference between the two terms. The word “deep” in deep learning refers to the depth of layers in a neural network. A neural network itself contains more than three layers and is considered a deep learning algorithm. However, a neural network containing only two or three layers is considered a basic neural network.
Why are neural networks important?
Neural networks are important because they help us solve real-world problems that are inherently complex and provide us the opportunity to improve our decision-making process. These networks have the ability to learn and model relationships that are nonlinear and complex, make generalizations inferences, reveal hidden relationships, highlight patterns and predictions, model highly volatile data, and various variances needed to predict events.
Areas in which neural networks can improve our decision-making process includes but is not limited to credit card and Medicare fraud detection, electoral load and energy demand forecasting, optimization of logistics for transportation networks, character and voice recognition, medical and disease diagnosis, targeted market, robotic control systems, financial predictions for stocks, currency, options, futures, and bankruptcy, computer vision to interpret raw photos and videos, process and quality control, and ecosystem evaluation.
Who uses neural networks?
Many industries utilize the benefits of neural networks and currently continue to do so. These industries include but are not limited to life sciences, manufacturing, public sector, baking, and retail.
Are there any disadvantages of neural networks? If so, then what are they?
This is an important question since neural network software recently have lots of hype around them. Yes, there are some disadvantages when it comes to neural networks. First, the “black box” nature of neural networks is a disadvantage of neural networks since you do not know how or why your neural network came up with a certain result.
Second, the duration of the development process takes time and is a complicated process. Third, neural networks require much more data than traditional machine learning algorithms. Lastly, neural networks are computationally expensive than traditional algorithms.