# Best Artificial Neural Network Software - Page 4 *By [Tian Lin](https://research.g2.com/insights/author/tian-lin)* Artificial neural network (ANN) software provides computational models that mimic the neural networks of the human brain, adapting to new information to automate complex tasks, support predictive analytics, and enable deep learning functionalities such as image recognition, natural language processing, and voice recognition across industries including healthcare, finance, and automotive. ### Core Capabilities of Artificial Neural Network Software To qualify for inclusion in the Artificial Neural Networks category, a product must: - Provide a network based on interconnected neural units to enable learning capabilities - Offer a backbone for deeper learning algorithms, including deep neural networks (DNNs) with multiple hidden layers - Link to data sources to feed the neural network information - Support model training, testing, and evaluation processes - Integrate with other machine learning (ML) and AI tools and frameworks - Enable scalability to handle large datasets and complex computations - Include documentation and support resources for users ### Common Use Cases for Artificial Neural Network Software Data scientists, ML engineers, and researchers use ANN software to build intelligent applications across a wide range of domains. Common use cases include: - Powering predictive analytics, anomaly detection, and customer behavior analysis in business applications - Enabling image recognition, NLP, and voice recognition through deep neural network architectures - Supporting healthcare diagnostics, financial fraud detection, and recommendation engine development ### How Artificial Neural Network Software Differs from Other Tools ANNs form the foundational layer for a wide range of deep learning algorithms, making them more fundamental than specialized ML tools focused on specific tasks. While [machine learning software](https://www.g2.com/categories/machine-learning) provides tools for capabilities like recommendation engines and pattern recognition, ANN platforms specifically focus on building and training interconnected neural unit networks that power deeper learning architectures including DNNs. ### Insights from G2 on Artificial Neural Network Software Based on category trends on G2, scalability for large datasets and flexibility in model architecture stand out as standout capabilities. These platforms deliver improvements in prediction accuracy and the ability to power complex deep learning applications as primary benefits of adoption. ## How Many Artificial Neural Network Software Products Does G2 Track? **Total Products under this Category:** 91 ### Category Stats (May 2026) - **Average Rating**: 4.27/5 - **New Reviews This Quarter**: 3 - **Buyer Segments**: Mid-Market 67% │ Small-Business 33% - **Top Trending Product**: AIToolbox (+0.011) *Last updated: May 18, 2026* ## How Does G2 Rank Artificial Neural Network Software Products? **Why You Can Trust G2's Software Rankings:** - 30 Analysts and Data Experts - 500+ Authentic Reviews - 91+ Products - Unbiased Rankings G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category. ## Which Artificial Neural Network Software Is Best for Your Use Case? - **Leader:** [AIToolbox](https://www.g2.com/products/aitoolbox/reviews) - **Highest Performer:** [Torch](https://www.g2.com/products/torch/reviews) - **Easiest to Use:** [AWS Deep Learning AMIs](https://www.g2.com/products/aws-deep-learning-amis/reviews) - **Top Trending:** [Keras](https://www.g2.com/products/keras/reviews) - **Best Free Software:** [H2O](https://www.g2.com/products/h2o/reviews) ## What Are the Top-Rated Artificial Neural Network Software Products in 2026? ### 1. [MARIANNMT](https://www.g2.com/products/mariannmt/reviews) Marian is an efficient, free Neural Machine Translation framework written in pure C++ with minimal dependencies. It is mainly being developed by the Microsoft Translator team. Many academic (most notably the University of Edinburgh and in the past the Adam Mickiewicz University in Poznań) and commercial contributors help with its development. **Who Is the Company Behind MARIANNMT?** - **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft) - **Year Founded:** 1975 - **HQ Location:** Redmond, Washington - **Twitter:** @microsoft (13,115,342 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®) - **Ownership:** MSFT ### 2. [MythWorx](https://www.g2.com/products/mythworx/reviews) MythWorx is an artificial general intelligence platform developing a biomimetic reasoning system. **Who Is the Company Behind MythWorx?** - **Seller:** [MythWorx](https://www.g2.com/sellers/mythworx) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/mythworx/ (9 employees on LinkedIn®) ### 3. [Neuraxle](https://www.g2.com/products/neuraxle/reviews) Neuraxle is a Machine Learning (ML) library for building clean machine learning pipelines using the right abstractions. Compatible with deep learning frameworks and the scikit-learn API, it can stream minibatches, use data checkpoints, build funky pipelines, and serialize models with custom per-step savers. **Who Is the Company Behind Neuraxle?** - **Seller:** [Neuraxio](https://www.g2.com/sellers/neuraxio) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 4. [NeuroReef](https://www.g2.com/products/neuroreef/reviews) NeuroReef Labs is at the forefront of medical artificial intelligence, developing neuro-inspired intelligent systems to enhance global healthcare outcomes. Their flagship products, MedAura and CareCortex.ai, are designed to provide evidence-based medical insights and streamline clinical workflows, respectively. Key Features and Functionality: - MedAura: Emulates human cognitive processes to deliver reliable medical information, utilizing the GRADE framework to ensure insights are based on the most current and credible evidence. - CareCortex.ai: An AI-powered platform that automates clinical documentation, generates EMR-ready notes, and includes features like dynamic evidence mapping, customizable workflows, real-time compliance validation, and audit logs. Primary Value and Solutions: NeuroReef Labs' products address critical challenges in healthcare by reducing administrative burdens, enhancing decision-making, and improving operational efficiency. By providing advanced AI solutions, they enable healthcare professionals to focus more on patient care, ultimately leading to better health outcomes and streamlined clinical processes. **Who Is the Company Behind NeuroReef?** - **Seller:** [NeuroReef](https://www.g2.com/sellers/neuroreef) - **Year Founded:** 2023 - **HQ Location:** Austin, US - **LinkedIn® Page:** https://www.linkedin.com/company/neuroreef-labs (5 employees on LinkedIn®) ### 5. [OpenNMT](https://www.g2.com/products/opennmt/reviews) OpenNMT initially focused on standard sequence to sequence models applied to machine translation, it has been extended to support many additional models and features. **Who Is the Company Behind OpenNMT?** - **Seller:** [OpenNMT](https://www.g2.com/sellers/opennmt) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 6. [PaddlePaddle](https://www.g2.com/products/paddlepaddle/reviews) An open-source deep learning platform with a simple API, trusted by the world's leading AI teams **Average Rating:** 4.5/5.0 **Total Reviews:** 1 **How Do G2 Users Rate PaddlePaddle?** - **Ease of Use:** 8.3/10 (Category avg: 8.1/10) - **Quality of Support:** 10.0/10 (Category avg: 8.0/10) **Who Is the Company Behind PaddlePaddle?** - **Seller:** [SaaSy Sales Management](https://www.g2.com/sellers/saasy-sales-management-0b57fef2-87f9-43e2-9e4d-07cbe5101e7d) - **HQ Location:** N/A - **Twitter:** @PaddleHQ (17,904 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 100% Enterprise ### 7. [Predicta Med](https://www.g2.com/products/predicta-med/reviews) Predicta Med is a pioneering digital health company dedicated to transforming the care of patients with autoimmune diseases. Their AI-driven platform integrates advanced clinical algorithms with real-time electronic medical record (EMR) data to streamline decision-making and personalize care throughout the patient journey. By harnessing artificial intelligence, Predicta Med empowers healthcare providers to make faster, more informed decisions, ultimately improving patient outcomes and operational efficiency. Key Features and Functionality: - PredictAI™: Utilizes AI to identify at-risk patients early and recommends subsequent evaluation steps, facilitating quicker and more accurate diagnoses across immune-related specialties. - PredictaChart™: Automatically generates concise patient chart summaries, highlighting essential clinical insights from complex EMRs, providing specialists with a clear and actionable overview of each patient. - PredictaMatch™: Intelligently matches eligible patients with relevant clinical trials, enhancing access to advanced therapies and supporting personalized treatment plans. Primary Value and Problem Solved: Predicta Med addresses the critical challenge of delayed diagnosis and treatment in autoimmune diseases, which can lead to disease progression and increased healthcare costs. By enabling early detection and providing actionable insights, their platform reduces the time to diagnosis from years or months to weeks. This not only improves patient outcomes but also enhances clinical efficiency and opens new revenue opportunities for healthcare providers. The platform's ability to integrate seamlessly with existing EMR systems ensures that specialists can manage more patients effectively while maintaining high standards of care. **Who Is the Company Behind Predicta Med?** - **Seller:** [Predicta Med](https://www.g2.com/sellers/predicta-med) - **HQ Location:** Tel Aviv, IL - **LinkedIn® Page:** https://www.linkedin.com/company/predicta-med (21 employees on LinkedIn®) ### 8. [PVmed](https://www.g2.com/products/pvmed/reviews) PVmed, established in May 2017, is an innovator in medical artificial intelligence, specializing in AI-enhanced diagnostic and therapeutic solutions based on medical imaging. The company offers comprehensive, self-developed software solutions that integrate advanced computer vision algorithms to assist healthcare professionals in diagnosing and treating various diseases more efficiently and accurately. Key Features and Functionality: - PV-iRT Intelligent Radiotherapy System: This multifunctional software supports the entire radiotherapy process, including image browsing, target contouring, plan design, plan evaluation, dose assessment, and efficacy analysis. Utilizing cutting-edge AI deep learning technology, it enhances the efficiency and precision of clinical diagnosis and treatment. - PV-iMIP Intelligent Medical Imaging Processing System: Designed to intelligently process mainstream imaging data, this system aids physicians in accurately locating lesions. It offers features such as multi-scale pixel-level segmentation, 3D positioning and quantitative analysis of nodules, rib suppression, thoracic extraction, virtual grid imaging processing, low-dose imaging processing, and multi-planar image reconstruction. - PV-iSA Intelligent Surgery Solution: This comprehensive solution integrates computer-aided diagnosis and medical imaging-assisted precision surgery. It includes the PV-iCAD Intelligent Computer-aided Diagnosis System for rapid pulmonary nodule detection and the PV-iCAS Intelligent Computer-aided Surgery System for automated segmentation of chest structures, intelligent surgical simulation, and safe surgical margin alerts. Primary Value and User Benefits: PVmed's solutions address critical challenges in medical imaging diagnostics and treatment by providing AI-driven tools that enhance accuracy, efficiency, and reliability. By automating complex processes such as lesion detection, target contouring, and surgical planning, these products reduce the workload on healthcare professionals, minimize human error, and improve patient outcomes. The integration of advanced AI technologies ensures that medical practitioners can deliver precise and personalized care, ultimately advancing the field of precision medicine. **Who Is the Company Behind PVmed?** - **Seller:** [PVmed](https://www.g2.com/sellers/pvmed) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) ### 9. [PyCaret](https://www.g2.com/products/pycaret/reviews) PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. **Who Is the Company Behind PyCaret?** - **Seller:** [PyCaret](https://www.g2.com/sellers/pycaret) - **Year Founded:** 2020 - **HQ Location:** Torento, CANADA - **LinkedIn® Page:** http://www.linkedin.com/company/pycaret (4 employees on LinkedIn®) ### 10. [Thakaa Med](https://www.g2.com/products/thakaa-med/reviews) Thakaa Med is a pioneering healthcare technology company specializing in AI-driven diagnostic solutions designed to enhance precision, reduce errors, and improve patient outcomes across various medical fields. By integrating advanced artificial intelligence into medical diagnostics, Thakaa Med aims to transform traditional healthcare practices into proactive, predictive, and personalized care models. Key Features and Functionality: - Dental IQ: An AI-powered tool that assists dental professionals in diagnostics and treatment planning, providing rapid and accurate analyses to streamline operations and improve patient communication. - Stroke IQ: Utilizes AI in neuroimaging to deliver faster and more precise stroke diagnoses, facilitating timely and effective treatment interventions. - Chest IQ: An AI-driven diagnostic tool for chest X-ray analysis, enhancing the detection and interpretation of thoracic conditions. - A.I. Factory: A comprehensive platform offering end-to-end medical data labeling, supporting research institutions in developing and refining AI models. Primary Value and User Solutions: Thakaa Med addresses critical challenges in healthcare by providing AI-assisted diagnostics that improve accuracy and efficiency. For dental practices, tools like Dental IQ offer rapid diagnostics and enhanced patient communication, leading to better engagement and outcomes. In neuroimaging and radiology, solutions like Stroke IQ and Chest IQ enable faster, more accurate diagnoses, allowing for timely interventions. Overall, Thakaa Med's suite of AI solutions empowers healthcare providers to deliver personalized, proactive care, ultimately enhancing patient satisfaction and health outcomes. **Who Is the Company Behind Thakaa Med?** - **Seller:** [Thakaa Med](https://www.g2.com/sellers/thakaa-med) - **Year Founded:** 2022 - **HQ Location:** Riyadh, SA - **LinkedIn® Page:** https://www.linkedin.com/company/thakaa-med (19 employees on LinkedIn®) ### 11. [Theano](https://www.g2.com/products/theano/reviews) Theano is a Python library that allows user to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently **Average Rating:** 3.1/5.0 **Total Reviews:** 4 **How Do G2 Users Rate Theano?** - **Ease of Use:** 4.2/10 (Category avg: 8.1/10) - **Quality of Support:** 5.0/10 (Category avg: 8.0/10) **Who Is the Company Behind Theano?** - **Seller:** [Theano](https://www.g2.com/sellers/theano) - **HQ Location:** Montreal, Quebec - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Who Uses This Product?** - **Company Size:** 50% Enterprise, 50% Mid-Market ### 12. [Thirdai](https://www.g2.com/products/thirdai/reviews) ThirdAI is an innovative artificial intelligence company dedicated to democratizing AI through software innovations. By developing hash-based processing algorithms for training and inference with neural networks, ThirdAI enables efficient AI model training on commodity hardware, such as x86 CPUs, achieving performance levels up to 15 times faster than leading GPUs. This approach challenges the prevailing notion that specialized processors are essential for deep learning tasks, making AI more accessible and cost-effective for a broader range of users. Key Features and Functionality: - Hash-Based Processing Algorithms: Utilizes innovative algorithms to enhance the efficiency of neural network training and inference. - Compatibility with Commodity Hardware: Enables AI model training on standard x86 CPUs, eliminating the need for specialized hardware. - Scalability: Offers solutions that scale effectively, accommodating the needs of both startups and large enterprises. - Cost-Effectiveness: Reduces the financial barriers to AI adoption by leveraging existing hardware infrastructure. Primary Value and User Solutions: ThirdAI's technology addresses the high costs and hardware dependencies traditionally associated with AI model training. By enabling efficient training on standard CPUs, it allows organizations to implement AI solutions without significant investment in specialized hardware. This democratization of AI empowers businesses of all sizes to harness the power of artificial intelligence, fostering innovation and competitiveness across various industries. **Who Is the Company Behind Thirdai?** - **Seller:** [Pocket LLM](https://www.g2.com/sellers/pocket-llm) - **Year Founded:** 2021 - **HQ Location:** Houston, US - **LinkedIn® Page:** http://linkedin.com/company/thirdai-corp (8 employees on LinkedIn®) ### 13. [Vastai Technologies](https://www.g2.com/products/vastai-technologies/reviews) Vastai Technologies is focused on developing AI chips for the optimization of computer vision and video processing. **Who Is the Company Behind Vastai Technologies?** - **Seller:** [Vastai Technologies](https://www.g2.com/sellers/vastai-technologies) - **Year Founded:** 2018 - **HQ Location:** Shanghai, CN - **LinkedIn® Page:** https://www.linkedin.com/company/vastaitech/ (99 employees on LinkedIn®) ### 14. [Vermeer](https://www.g2.com/products/vermeer/reviews) Vermeer is a mixed reality and AI-enabled visualization planning tool. **Who Is the Company Behind Vermeer?** - **Seller:** [Vermeer](https://www.g2.com/sellers/vermeer) - **Year Founded:** 2019 - **HQ Location:** Brooklyn, US - **LinkedIn® Page:** https://www.linkedin.com/company/vermeerapp (27 employees on LinkedIn®) ### 15. [Visnet](https://www.g2.com/products/visnet/reviews) Visnet is an advanced artificial intelligence framework designed to streamline the development and deployment of neural network models across various platforms. At its core, Visnet offers a headless, multi-compatible, and universal interface that supports a wide range of AI applications, particularly in natural language processing (NLP) and deep vision systems. This comprehensive framework enables seamless integration and efficient management of AI models, catering to diverse industry needs. Key Features and Functionality: - Universal Compatibility: Visnet's framework is designed to be universally compatible, housing a suite of service-oriented architecture (SOA) neural network models related to NLP and deep vision systems. - Modular Frontend: The platform offers a serverless, multi-compatible frontend that supports Vercel deployment, ensuring flexibility and scalability. - High-Performance Gateway: Visnet includes a universal ASGI gateway equipped with DDOS protection and IP filtering, enhancing security and performance. - Authentication Protocol Layer: The framework incorporates robust authentication protocols, including OAuth 2.0, PIN access, RSA encryption, and blacklisting capabilities. - Core AI Models: Visnet provides a range of core AI models, such as translation services, license plate recognition, and facial feature matching, addressing various application needs. Primary Value and Solutions: Visnet addresses complex challenges in surveillance, autonomous drone inspections, and advanced image and video analysis. By offering sophisticated facial feature recognition, the framework enables defense-grade authentication and registration, capable of analyzing facial features for identification, eye-tracking, emotion, and age detection simultaneously with high efficiency. In the realm of structural inspections, Visnet facilitates autonomous drone inspections to detect and identify structural defects in concrete structures, effectively managing multiple defects concurrently. Additionally, the framework supports live audio transcription with high-performance, handcrafted models that offer multilingual support, enhancing accessibility and usability across different languages. Furthermore, Visnet's license plate recognition system can detect and identify up to 20 vehicles in a single frame at 30 frames per second, enabling the recognition of up to 600 vehicles per second, which is particularly beneficial for traffic management and security applications. By integrating these advanced AI capabilities into a single, cohesive framework, Visnet empowers industries to harness the full potential of artificial intelligence, driving innovation and efficiency in their operations. **Who Is the Company Behind Visnet?** - **Seller:** [Visnet](https://www.g2.com/sellers/visnet) - **Year Founded:** 2022 - **HQ Location:** Hyderabad, IN - **LinkedIn® Page:** https://in.linkedin.com/company/visnet-ai-private-limited (3 employees on LinkedIn®) ### 16. [Walking Recognition](https://www.g2.com/products/walking-recognition/reviews) Turn your CCTV archives
into a fingerprint database Identify individuals in crowds from their unique gross motor coordination, without the use of face recognition. Our AI analyzes and recognizes walking patterns, which are just as unique as fingerprints. -Use your existing CCTV system
or autonomous drones -Protect personal data by not processing or storing sensitive biometric features Our technology operates effectively under various conditions, including: -Daylight or darkness -When individuals are masked -If the video resolution is too low
for face recognition -Supercharge your video analytics with our computer vision expertise and award-winning movement analysis AI. What else can our AI
uncover from live video footage? -Detect suspicious behavior, such as signs of nervousness or fleeing in airports, offices, and other public spaces -Flag unauthorized use of NFC and RFID cards or tailgating at access control points -Identify accidents or people in distress in travel hubs, indus-trial or sports facilities -Predict and prevent shoplifting with object and pose recognition Detailed product description Cursor Insight's CCTV gait recognition technology represents a pioneering advancement in the field of surveillance and security. Utilizing advanced algorithms and machine learning models, our deep-tech application analyzes live video footage to discern individuals' unique gross motor coordination, enabling accurate recognition of people based on their distinctive gait patterns and body dimensions. Our system's versatility is a key strength, applicable across various scenarios, from airports and banks to industrial and sports facilities. It can identify people and suspicious behavior, flag unauthorized access attempts, and detect accidents or distress situations. Leveraging existing CCTV archives, our technology transforms them into a valuable fingerprint database, extracting hundreds of movement features without compromising personal data privacy. Additionally, our system can integrate seamlessly with autonomous drone monitoring systems, providing real-time analysis of a person’s unique movement patterns on live video footage. Our technology operates effectively under various conditions, including 24-7 day and night surveillance, situations where individuals are masked, and instances where the resolution of the footage is too low. We strive for innovation, as reflected in our award-winning machine learning technology, praised for its precision in user identification and classification. With a team bringing together over a century of collective experience in machine learning and biometrics, we aim to advance security and surveillance standards, striving for excellence across all facets of our technology. We value our expertise in data preprocessing, utilizing a reverse engineering approach to ensure the accuracy of recreated movements. Our proprietary feature extraction utilizes a universal feature space adapted over 15 years to provide deeper insights into motor program patterns. Our Random Forest-based Multi-Round Screening Method ensures optimal feature selection, identifying crucial subsets for efficient machine learning models. Through our partnership with the Hungarian National Institute of Clinical Neurosciences, we have access to a dedicated Motion Lab equipped with state-of-the-art technology, facilitating efficient insights from laboratory-recorded data. Central to our approach is the protection of personal data and privacy. Unlike traditional biometric systems, we focus solely on analyzing gross motor coordination and body dimensions. We avoid processing or storing sensitive biometric data, such as facial images, to ensure compliance with privacy regulations. **Who Is the Company Behind Walking Recognition?** - **Seller:** [Cursor Insight](https://www.g2.com/sellers/cursor-insight) - **Year Founded:** 2013 - **HQ Location:** London, GB - **Twitter:** @cursorinsight (1,455 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/cursor-insight/?viewAsMember=true (22 employees on LinkedIn®) ## What Is Artificial Neural Network Software? [Deep Learning Software](https://www.g2.com/categories/deep-learning) --- ## How Do You Choose the Right Artificial Neural Network Software? ### What You Should Know 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](https://www.g2.com/categories/financial-services) to [agriculture](https://www.g2.com/categories/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](https://www.g2.com/categories/chatbots) and automated [insurance claims management software](https://www.g2.com/categories/insurance-claims-management). #### 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](https://www.g2.com/categories/natural-language-processing-nlp): 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](https://www.g2.com/categories/image-recognition): 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](https://www.g2.com/categories/chatbots) **:** 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](https://www.g2.com/categories/bot-platforms) **:** 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.