Learn More About Artificial Neural Network Software
What is Artificial Neural Network Software?
Artificial neural network (ANN) software, often used synonymously with deep learning software, automates tasks for users by leveraging artificial neural networks to produce an output, often in the form of a prediction. Although some will distinguish between ANNs and deep learning (arguing that the latter refers to the training of ANNs), this guide will use the terms interchangeably. These solutions are typically embedded into various platforms and have use cases across various industries. Solutions built on artificial neural networks improve the speed and accuracy of desired outputs by constantly refining them as the application digests more training data.
Deep learning software improves processes and introduces efficiency to multiple industries, from financial services to agriculture. Applications of this technology include process automation, customer service, security risk identification, and contextual collaboration. Notably, end users of deep learning-powered applications do not interact with the algorithm directly. Rather, deep learning powers the backend of the artificial intelligence (AI) that users interact with. Some prime examples include chatbots software and automated insurance claims management software. Teams that compare neural network libraries for object detection and visual recognition in production logistics, such as scanning labels or inspecting packages, usually start with the convolutional models covered below. Based on G2 reviews, users, administrators, and consultants evaluate artificial neural network software by comparing preconfigured framework environments, GPU support for faster training, and version compatibility issues that still take manual fixes.
What Types of Artificial Neural Network Software Exist?
There are two main types of artificial neural network software: recurrent neural networks (RNNs) and convolutional neural networks (CNNs). The type of neural network doesn’t generally affect the end product that customers will use but might affect the accuracy of the outcome. For example, whether an image recognition tool is built using CNNs or RNNs matters little to the companies that employ it to deal with customers. Companies care more about the potential impact of deploying a well-made virtual assistant to their business model.
Convolutional neural networks (CNNs)
Convolutional neural networks (CNNs) extract features directly from data, such as images, eliminating the need for manual feature extraction. Manual feature extraction would require the data scientist to go in and determine the various components and aspects of the data. With this technology, the neural network determines this by itself. None of the features are pre-trained; instead, they are learned by the network when it trains on the given set of images. This automated feature extraction characteristic makes deep learning models highly effective for object classification and other computer vision applications.
Recurrent neural networks (RNNs)
Recurrent neural networks (RNNs) use sequential data or time series data. These deep learning algorithms are commonly used for ordinal or temporal problems. They are primarily leveraged using time series data to make predictions about future events, such as sales forecasting.
What are the Common Features of Artificial Neural Network Software?
Core features within artificial neural network software help users improve their applications, allowing for them to transform their data and derive insights from it in the following ways:
Data: Connection to third-party data sources is the key to the success of a machine learning application. To function and learn properly, the algorithm must be fed large amounts of data. Once the algorithm has digested this data and learned the proper answers to typically asked queries, it can provide users with an increasingly accurate answer set. Often, deep learning applications offer developers sample datasets to build their applications and train their algorithms. These prebuilt datasets are crucial for developing well-trained applications because the algorithm needs to see a ton of data before it’s ready to make correct decisions and give correct answers. In addition, some solutions will include data enrichment capabilities, like annotating, categorizing, and enriching datasets.
Algorithms: The most crucial feature of any machine learning offering, deep learning or otherwise, is the algorithm. It is the foundation on which everything else is based. Solutions either provide prebuilt algorithms or allow developers to build their own in the application.
What are the Benefits of Artificial Neural Network Software?
Artificial neural network software is useful in many different contexts and industries. For example, AI-powered applications typically use deep learning algorithms on the backend to provide end users with answers to queries.
Application development: Artificial neural network software drives the development of AI applications that streamline processes, identify risks, and improve effectiveness.
Efficiency: Deep learning-powered applications are constantly improving because of the recognition of their value and the need to stay competitive in the industries in which they are used. They also increase the efficiency of repeatable tasks. A prime example of this can be seen in eDiscovery, where deep learning has created massive leaps in the efficiency with which legal documents are looked through, and relevant ones are identified.
Risk reduction: Risk reduction is one of the most significant use cases in financial services for machine learning applications. Deep learning-powered AI applications identify potential risks and automatically flag them based on historical data of past risky behaviors. This eliminates the need for manual identification of risks, which is prone to human error. Deep learning-driven risk reduction is useful in the insurance, finance, and regulation industries, among others.
Who Uses Artificial Neural Network Software?
AI software has applications across nearly every industry. Some industries that benefit from deep learning applications include financial services, cybersecurity, recruiting, customer service, energy, and regulation.
Marketing: Deep learning-powered marketing applications help marketers identify content trends, shape content strategy, and personalize marketing content. Marketing-specific algorithms segment customer bases, predict customer behavior based on past behavior and customer demographics, identify high potential prospects, and more.
Finance: Financial services institutions are increasing their use of machine learning-powered applications to stay competitive with others in the industry who are doing the same. Through robotic process automation (RPA) applications, which are typically powered by machine learning algorithms, financial services companies are improving the efficiency and effectiveness of departments, including fraud detection, anti-money laundering, and more. However, the departments in which these applications are most effective are ones in which there is a great deal of data to manage and many repeatable tasks that require little creative thinking. Some examples may include trawling through thousands of insurance claims and identifying ones with a high potential to be fraudulent. The process is similar, and the machine learning algorithm can digest the data to achieve the desired outcome much quicker.
Cybersecurity: Deep learning algorithms are being deployed in security applications to better identify threats and automatically deal with them. The adaptive nature of certain security-specific algorithms allows applications to tackle evolving threats more easily.
What are the Alternatives to Artificial Neural Network Software?
Alternatives to artificial neural network software that can replace it either partially or completely include:
Natural language processing (NLP) software: Businesses focused on language-based use cases (e.g., examining large swaths of review data to better understand the reviewers’ sentiment) can also look to NLP solutions, such as natural language understanding software, for solutions specifically geared toward this type of data. Use cases include finding insights and relationships in text, identifying the language of the text, and extracting key phrases from a text.
Image recognition software: For computer vision or image recognition, companies can adopt image recognition software. These tools can enhance their applications with features such as image detection, face recognition, image search, and more.
Software Related to Artificial Neural Network Software
Related solutions that can be used together with artificial neural network software include:
Chatbots software: Businesses looking for an off-the-shelf conservational AI solution can leverage chatbots. Tools specifically geared toward chatbot creation helps companies use chatbots off the shelf, with little to no development or coding experience necessary.
Bot platforms software: Companies looking to build their own chatbot can benefit from bot platforms, which are tools used to build and deploy interactive chatbots. These platforms provide development tools such as frameworks and API toolsets for customizable bot creation.
Challenges with Artificial Neural Network Software
Software solutions can come with their own set of challenges.
Automation pushback: One of the biggest potential issues with applications powered by ANNs lies in the removal of humans from processes. This is particularly problematic when looking at emerging technologies like self-driving cars. By completely removing humans from the product development lifecycle, machines are given the power to decide in life or death situations.
Data quality: With any deployment of AI, data quality is key. As such, businesses must develop a strategy around data preparation, ensuring there are no duplicate records, missing fields, or mismatched data. A deployment without this crucial step can result in faulty outputs and questionable predictions.
Data security: Companies must consider security options to ensure the correct users see the correct data. They must also have security options that allow administrators to assign verified users different levels of access to the platform.
Which Companies Should Buy Machine Learning Software?
Pattern recognition can help businesses across industries. Effective and efficient predictions can help these businesses make data-informed decisions, such as dynamic pricing based upon a range of data points.
Retail: An e-commerce site can leverage a deep learning API to create rich, personalized experiences for every user.
Finance: A bank can use this software to improve its security capabilities by identifying potential problems, such as fraud, early on.
Entertainment: Media organizations are able to leverage recommendation algorithms to serve their customers with relevant and related content. With this enhancement, businesses can continue to capture the attention of their viewers.
How to Buy Artificial Neural Network Software
Requirements Gathering (RFI/RFP) for Artificial Neural Network Software
If a company is just starting out and looking to purchase their first artificial neural network software, wherever they are in the buying process, g2.com can help select the best machine learning software for them.
Taking a holistic overview of the business and identifying pain points can help the team create a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features, including budget, features, number of users, integrations, security requirements, cloud or on-premises solutions, and more. Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a machine learning platform.
Compare Artificial Neural Network Software Products
Create a long list
From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison, after the demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.
Create a short list
From the long list of vendors, it is advisable to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various solutions.
Conduct demos
To ensure the comparison is thoroughgoing, the user should demo each solution on the short list with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.
Selection of Machine Learning Software
Choose a selection team
Before getting started, creating a winning team that will work together throughout the entire process, from identifying pain points to implementation, is crucial. The software selection team should consist of organization members with the right interest, skills, and time to participate in this process. A good starting point is to aim for three to five people who fill roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator, or security administrator. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities.
Negotiation
Prices on a company's pricing page are not always fixed (although some companies will not budge). It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.
Final decision
After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.
What Does Artificial Neural Network Software Cost?
Artificial neural network software is generally available in different tiers, with the more entry-level solutions costing less than the enterprise-scale ones. The former will usually lack features and may have caps on usage. Vendors may have tiered pricing, in which the price is tailored to the users’ company size, the number of users, or both. This pricing strategy may come with some degree of support, either unlimited or capped at a certain number of hours per billing cycle.
Once set up, they do not often require significant maintenance costs, especially if deployed in the cloud. As these platforms often come with many additional features, businesses looking to maximize the value of their software can contract third-party consultants to help them derive insights from their data and get the most out of the software.
Return on Investment (ROI)
Businesses decide to deploy deep learning software to derive some degree of an ROI. As they are looking to recoup the losses from the software purchase, it is critical to understand the costs associated with it. As mentioned above, these platforms are typically billed per user, sometimes tiered depending on the company size.
More users will typically translate into more licenses, which means more money. Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the platform.
Artificial Neural Network Software Trends
Automation
The adoption of deep learning is related to a broader trend around automation. RPA is driving an increased interest in the deep learning space because machine learning enables RPA. RPA is gaining in popularity across multiple verticals, being particularly useful in industries heavy on data entry, like financial services, because of its ability to process data and increase efficiency.
Human vs. machine
With the adoption of deep learning and the automation of repetitive tasks, businesses can deploy their human workforce to more creative projects. For example, if an algorithm automatically displays personalized advertisements, the human marketing team can work on producing creative material.
Most Popular FAQs
Which artificial neural network software has the best reviews?
The best-reviewed artificial neural network software pairs strong ratings with steady praise, and the Fall 2026 G2 Grid® Report puts two preconfigured cloud environments at the top. The most trusted neural network libraries for ML researchers in user reviews are Keras and PyTorch, both rated 4.6 out of 5 and praised for fast experimentation.
Is there free neural network software?
Yes. Open-source libraries cost nothing to license, and PyTorch reviewers often point to its free, open-source license as part of its value, with GPU or cloud resources as the main expense on larger projects. Preconfigured cloud environments work differently: Reviewers of Google Cloud Deep Learning VM Image note that the image itself is free, but the GPU instances it runs on are billed by usage, and many say those costs climb quickly when instances are left running. In the past year, 37 of the 90 artificial neural network reviews on G2 raised cost as a downside, mostly about GPU compute rather than software licenses.
What are the best artificial neural network frameworks for computer vision engineers training models on custom datasets without deep research expertise?
For engineers without a research background, reviews favor artificial neural network tools that ship ready-made network architectures and visual training feedback for machine learning, image classification, and deep learning prototyping.
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NVIDIA Deep Learning GPU Training System (DIGITS): Many reviewers describe training image classification, object detection, and segmentation models quickly on GPUs, while several find installation difficult.
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Keras: Reviewers praise building image classifiers in a few lines of code with pretrained models such as VGG and ResNet, though many say advanced customization is hard.
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Caffe: Reviewers credit fast training and solid convolutional network support for image classification, while several say custom layers are hard to add.
What are the most reliable artificial neural network frameworks based on reviews from AIML engineers deploying at scale in industrial automation?
Reviews from industrial automation teams are rare: Only 2 of the 90 artificial neural network reviews from the past year come from logistics or machinery companies, so reliability when deploying at scale is the clearer signal, and preconfigured cloud environments lead it.
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Google Cloud Deep Learning VM Image: Reviewers describe identical, version-pinned environments on any machine shape, from a CPU instance for data preparation to multi-GPU training, which keeps results reproducible.
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Google Cloud Deep Learning Containers: Reviewers say the same container runs from a local workstation to training clusters and production endpoints, though many note multi-gigabyte images that slow deployments.
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AWS Deep Learning AMIs: Reviewers credit an out-of-the-box GPU and framework stack that cuts configuration failures, with heavy images and version management as recurring complaints.
Most reviews don't list a job title, so this reflects feedback from engineering and data science teams in general rather than AIML engineers alone.
Which software is used for artificial neural networks?
Artificial neural networks are usually built with open-source deep learning libraries, run on preconfigured cloud environments that come with GPU drivers and frameworks installed, or trained through no-code AutoML tools for teams without data scientists. Among the most used neural network deep learning libraries, researchers mostly compare PyTorch, TensorFlow, and JAX, and user reviews on G2 cover PyTorch (4.6 out of 5 across 23 reviews) because TensorFlow and JAX aren't listed in this category. For PyTorch, TensorFlow, and JAX reviews, researchers comparing machine learning libraries often weigh hands-on feedback alongside Stack Overflow and Kaggle survey usage data, and the Keras vs. PyTorch comparison puts the two libraries listed in this category side by side. This question reflects a dominant pattern across search results for this category rather than a single source. Search results describe neural network software as the libraries, runtimes, and development tools used to design, train, and deploy networks, with older simulators largely replaced by general development environments. Recent reviews lean heavily toward the cloud option: 81 of the 90 reviews from the past year cover preconfigured cloud images and containers.
Small Business FAQs
What is the most affordable artificial neural network software for SMBs?
The most affordable artificial neural network software for small businesses starts with open-source libraries, since the bigger cost is usually GPU compute.
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PyTorch: Reviewers value that it is free and open source, so spending goes to compute instead of licenses.
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Keras: Reviewers describe an open-source library that turns complex networks into a few lines of code.
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Google Cloud Deep Learning VM Image: The image costs nothing, but reviewers warn that GPU instances left running drive up bills.
What is the best artificial neural network software for startups?
For startups, the top artificial neural network tools for startups are the ones that skip infrastructure and hiring hurdles. Startups looking for the best neural network frameworks for fast inference on resource-constrained devices, such as phones, often start with pretrained or compact models
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Neuton AutoML: Reviewers say analysts build models in minutes with no code, while several want more data sources than CSV uploads.
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MLKit: Mobile developers describe adding pretrained vision and text features to Android apps with little machine learning knowledge.
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H2O: Reviewers praise quick prototyping and AutoML that tests many models with a few lines of code, with documentation a common complaint.
Which artificial neural network software is the most user-friendly for startups?
Among neural network software for startups, these tools draw consistent ease-of-use praise.
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AWS Deep Learning AMIs: Reviewers describe a zero-config environment that is ready for GPU work right away.
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MLKit: Reviewers call it easy to integrate, with samples and predefined functions that help beginners.
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Microsoft Cognitive Toolkit (Formerly CNTK): Reviewers say it is easy to install and start with, even with minimal coding experience, though several want more community support.
How can a small business keep GPU costs under control with neural network software?
Plan for compute, not licenses, when choosing small business artificial neural network software. Cost is one of the most common complaints in this category: 37 of the 90 reviews from the past year raise it, mostly about GPU instances billed while they run. Many reviewers say charges climb when GPU instances are left running overnight, so shutting idle instances down matters as much as the hourly rate. Reviewers also describe starting experiments on smaller CPU instances and moving to GPUs only for heavy training, which keeps early work cheap.
Do small businesses need data scientists to use neural network software?
Not always. Several artificial neural network tools for small businesses are built for analysts and developers rather than data scientists. Neuton AutoML reviewers repeatedly praise building models with no coding, and several say analysts use it without pulling in data scientists. Caffe reviewers note that its built-in code handles classification tasks without much programming, and MLKit reviewers describe adding pretrained vision features to mobile apps without deep machine learning knowledge. Custom architectures are different: Reviews of code-first libraries still describe a learning curve for advanced models.
Enterprise FAQs
What is the best-rated artificial neural network software for tech enterprises?
Among enterprise artificial neural network software, these products combine strong ratings with steady enterprise feedback.
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H2O: Rated 4.5 out of 5 across 24 reviews, with reviewers praising AutoML, ease of use, and responsive support.
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NVIDIA Deep Learning AMI: Rated 4.5 across 10 reviews, with reviewers crediting a pre-optimized GPU software stack and strong performance.
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Microsoft Cognitive Toolkit (Formerly CNTK): Rated 4.2 across 22 reviews, many from enterprise teams, with praise for scaling across business needs and support for multiple programming languages.
What is the most reliable artificial neural network software for enterprises?
In artificial neural network reviews from enterprise teams, reliability usually means consistent environments and stable performance.
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Google Cloud Deep Learning Containers: Reviewers praise standardized environments that reduce deployment issues and make results reproducible across teams.
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NVIDIA Deep Learning AMI: Reviewers describe a stack tuned for NVIDIA GPUs that performs well, with UI complexity a common complaint.
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H2O: Reviewers call it reliable and easy to deploy, with good integration between development and production.
What is the best-reviewed artificial neural network software for enterprise app integration?
For enterprise neural network platforms, integration reviews favor tools that fit the cloud and programming languages a company already uses.
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Google Cloud Deep Learning Containers: Reviewers describe deploying the same containers on Vertex AI, GKE, and Compute Engine, with tight Google Cloud integration.
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Microsoft Cognitive Toolkit (Formerly CNTK): Reviewers value libraries for multiple programming languages, including Python and .NET.
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H2O: Reviewers mention R and Python options and a visual interface over a complex backend, while documentation is a frequent complaint.
How can enterprises standardize deep learning environments across data science teams?
Most recent reviews of enterprise artificial neural network software describe preconfigured images and containers as the way to give every data scientist the same framework, driver, and CUDA versions. Reviewers say standard images make results easier to reproduce and remove the setup work that used to open every project. NVIDIA Deep Learning AMI reviewers describe a pre-optimized stack for NVIDIA GPUs that can be tuned to each requirement. The tradeoff is maintenance: 41 of the 90 reviews from the past year mention version or dependency issues, and several reviewers pin versions or customize base images to keep projects consistent.
What should enterprises evaluate in artificial neural network software?
Based on recent G2 reviews, enterprises should evaluate the following in enterprise artificial neural network software:
- Version compatibility: 41 of the 90 reviews from the past year mention version or dependency issues, even with preconfigured environments.
- Compute cost: 37 of the 90 reviews raise cost as a downside, mostly GPU instances billed while they run.
- Image size: Many container reviewers say multi-gigabyte images slow first pulls, cold starts, and CI/CD deployments.
- Cloud skills: Many reviewers say teams new to the underlying cloud face a learning curve with permissions, networking, and machine types.
- Customization: Several reviewers say preconfigured images and high-level libraries are harder to adapt for specialized needs.
Last Updated on October 12, 2026
