Best Artificial Neural Network Software
Artificial neural networks (ANN) are computational models designed to mimic the neural networks found in the human brain. They adapt to new information and learn to make decisions based on it, theoretically mirroring human decision-making processes. ANNs are widely used across various industries, including healthcare, finance, automotive, and technology, to automate complex tasks, enhance decision-making, and improve operational efficiency.
ANNs require a data pool as a baseline for learning. The more data they have, the more connections they can establish. This, in turn, enhances their learning capabilities. As ANNs learn, they can consistently provide accurate outputs aligned with user-defined solutions. Businesses use ANNs for predictive analytics, anomaly detection, customer behavior analysis, and more.
A subset of ANNs is deep neural networks (DNN). They are characterized by multiple hidden layers between the input and output layers. These networks are essential for building intelligent applications with deep learning functionalities like image recognition, natural language processing (NLP), and voice recognition. DNNs are particularly useful in applications requiring high accuracy and the ability to learn complex patterns from large datasets.
ANNs form the foundation for various deep learning algorithms, including but not limited to image recognition, NLP, voice recognition, autonomous systems, recommendation engines, and generative models. For example, in healthcare, ANNs help in diagnosing diseases from medical images, while in finance, they are used for fraud detection and risk management.
To qualify for inclusion in the Artificial Neural Networks category, a product must:
Best Artificial Neural Network Software At A Glance
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AIToolbox is a comprehensive Swift framework designed to facilitate the development and implementation of artificial intelligence algorithms. It offers a suite of AI modules that cater to various mach
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Keras is a neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
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ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in a browser.
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Deep Learning VM Image Preconfigured VMs for deep learning applications.
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gobrain is a neural networks written in go that includes just basic Neural Network functions such as Feed Forward and Elman Recurrent Neural Network.
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Neuton (https://neuton.ai), a new AutoML solution, allows users to build compact AI models with just a few clicks and without any coding. Neuton also happens to be the most EXPLAINABLE Neural Network
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.
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.
