# Best Artificial Neural Network Software *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. ## Category Overview **Total Products under this Category:** 91 ## Trust & Credibility Stats **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. ## Best Artificial Neural Network Software At A Glance - **Leader:** [AIToolbox](https://www.g2.com/products/aitoolbox/reviews) - **Highest Performer:** [Torch](https://www.g2.com/products/torch/reviews) - **Easiest to Use:** [Keras](https://www.g2.com/products/keras/reviews) - **Top Trending:** [Keras](https://www.g2.com/products/keras/reviews) - **Best Free Software:** [H2O](https://www.g2.com/products/h2o/reviews) ## Top-Rated Products (Ranked by G2 Score) ### 1. [AIToolbox](https://www.g2.com/products/aitoolbox/reviews) 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 machine learning tasks, making it a valuable resource for developers and researchers working within the Swift ecosystem. Key Features and Functionality: - Graphs and Trees: Provides data structures and algorithms for constructing and manipulating graphs and trees, essential for tasks like decision-making processes and hierarchical data representation. - Support Vector Machines (SVMs): Includes tools for implementing SVMs, enabling classification and regression analysis by finding optimal hyperplanes in high-dimensional spaces. - Neural Networks: Offers components to build and train neural networks, facilitating deep learning applications such as image and speech recognition. - Principal Component Analysis (PCA): Contains modules for dimensionality reduction through PCA, aiding in data visualization and noise reduction. - K-Means Clustering: Provides algorithms for partitioning datasets into clusters, useful in pattern recognition and data mining. - Genetic Algorithms: Includes tools for optimization problems using genetic algorithms, simulating natural selection processes to find optimal solutions. Primary Value and User Solutions: AIToolbox addresses the need for a native Swift library that encompasses a broad range of AI functionalities. By integrating multiple machine learning modules into a single framework, it simplifies the development process for Swift developers, eliminating the need to rely on external libraries or languages. This consolidation enhances efficiency, promotes code consistency, and accelerates the deployment of AI-driven applications on Apple platforms. **Average Rating:** 4.4/5.0 **Total Reviews:** 33 **User Satisfaction Scores:** - **Ease of Use:** 8.8/10 (Category avg: 8.1/10) - **Quality of Support:** 8.9/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [AIToolbox](https://www.g2.com/sellers/aitoolbox) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 55% Small-Business, 39% Mid-Market #### Pros & Cons **Pros:** - Ease of Use (10 reviews) - Model Variety (5 reviews) - AI Technology (4 reviews) - Integrations (3 reviews) - Features (2 reviews) **Cons:** - Inaccuracy (3 reviews) - Limited Features (2 reviews) - AI Limitations (1 reviews) - Compatibility Issues (1 reviews) - Complex Setup (1 reviews) ### 2. [Keras](https://www.g2.com/products/keras/reviews) Keras is a neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. **Average Rating:** 4.6/5.0 **Total Reviews:** 64 **User Satisfaction Scores:** - **Ease of Use:** 8.9/10 (Category avg: 8.1/10) - **Quality of Support:** 7.8/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Keras](https://www.g2.com/sellers/keras) - **Year Founded:** 2016 - **HQ Location:** N/A - **Twitter:** @keras (26 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/keras/ (22 employees on LinkedIn®) **Reviewer Demographics:** - **Who Uses This:** Data Scientist - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 38% Small-Business, 32% Mid-Market ### 3. [Microsoft Cognitive Toolkit (Formerly CNTK)](https://www.g2.com/products/microsoft-cognitive-toolkit-formerly-cntk/reviews) Microsoft Cognitive Toolkit is an open-source, commercial-grade toolkit that empowers user to harness the intelligence within massive datasets through deep learning by providing uncompromised scaling, speed and accuracy with commercial-grade quality and compatibility with the programming languages and algorithms already use. **Average Rating:** 4.2/5.0 **Total Reviews:** 22 **User Satisfaction Scores:** - **Ease of Use:** 8.0/10 (Category avg: 8.1/10) - **Quality of Support:** 8.1/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft) - **Year Founded:** 1975 - **HQ Location:** Redmond, Washington - **Twitter:** @microsoft (13,105,844 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®) - **Ownership:** MSFT **Reviewer Demographics:** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 68% Enterprise, 27% Small-Business #### Pros & Cons **Pros:** - Workflow Efficiency (1 reviews) **Cons:** - Complexity Issues (1 reviews) - Learning Curve (1 reviews) ### 4. [AWS Deep Learning AMIs](https://www.g2.com/products/aws-deep-learning-amis/reviews) The AWS Deep Learning AMIs is designed to equip data scientists, machine learning practitioners, and research scientists with the infrastructure and tools to accelerate work in deep learning, in the cloud, at any scale. **Average Rating:** 4.3/5.0 **Total Reviews:** 19 **User Satisfaction Scores:** - **Ease of Use:** 9.2/10 (Category avg: 8.1/10) - **Quality of Support:** 8.5/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) - **Year Founded:** 2006 - **HQ Location:** Seattle, WA - **Twitter:** @awscloud (2,223,984 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®) - **Ownership:** NASDAQ: AMZN **Reviewer Demographics:** - **Top Industries:** Computer Software - **Company Size:** 42% Enterprise, 32% Small-Business ### 5. [PyTorch](https://www.g2.com/products/pytorch/reviews) PyTorch is an open-source machine learning framework that accelerates the transition from research prototyping to production deployment. Developed by Meta AI and now governed by the PyTorch Foundation under the Linux Foundation, PyTorch is widely used for applications in computer vision, natural language processing, and more. Its dynamic computation graph and intuitive Python interface make it a preferred choice for researchers and developers aiming to build and deploy deep learning models efficiently. Key Features and Functionality: - Dynamic Computation Graph: Allows for flexible and efficient model building, enabling changes to the network architecture during runtime. - Tensors and Autograd: Utilizes tensors as fundamental data structures, similar to NumPy arrays, with support for automatic differentiation to streamline the computation of gradients. - Neural Network API: Provides a modular framework for constructing neural networks with pre-defined layers, activation functions, and loss functions, facilitating the creation of complex models. - Distributed Training: Offers native support for distributed training, optimizing performance across multiple GPUs and nodes, which is essential for scaling large models. - TorchScript: Enables the transition from eager execution to graph execution, allowing models to be serialized and optimized for deployment in production environments. - TorchServe: A tool for deploying PyTorch models at scale, supporting features like multi-model serving, logging, metrics, and RESTful endpoints for application integration. - Mobile Support (Experimental): Extends PyTorch capabilities to mobile platforms, allowing models to be deployed on iOS and Android devices. - Robust Ecosystem: Supported by an active community, PyTorch offers a rich ecosystem of tools and libraries for various domains, including computer vision and reinforcement learning. - ONNX Support: Facilitates exporting models in the Open Neural Network Exchange (ONNX) format for compatibility with other platforms and runtimes. Primary Value and User Solutions: PyTorch's primary value lies in its ability to provide a seamless path from research to production. Its dynamic computation graph and user-friendly interface allow for rapid prototyping and experimentation, enabling researchers to iterate quickly on model designs. For developers, PyTorch's support for distributed training and tools like TorchServe simplify the deployment of models at scale, reducing the time and complexity associated with bringing machine learning models into production. Additionally, the extensive ecosystem and community support ensure that users have access to a wide range of resources and tools to address various machine learning challenges. **Average Rating:** 4.5/5.0 **Total Reviews:** 21 **User Satisfaction Scores:** - **Ease of Use:** 8.6/10 (Category avg: 8.1/10) - **Quality of Support:** 7.9/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Jetware](https://www.g2.com/sellers/jetware-c6839872-6292-4a7b-973d-ac6da2ceaa45) - **Year Founded:** 2017 - **HQ Location:** Roma, IT - **Twitter:** @jetware_io (26 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/jetware.org/about/ (2 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Computer Software - **Company Size:** 41% Small-Business, 41% Mid-Market #### Pros & Cons **Pros:** - Cloud Storage (1 reviews) - Documentation (1 reviews) - Ease of Use (1 reviews) - Intuitive (1 reviews) - Problem Solving (1 reviews) **Cons:** - Complexity (1 reviews) - Difficult Learning (1 reviews) - Difficult Navigation (1 reviews) ### 6. [Knet](https://www.g2.com/products/knet/reviews) Knet (pronounced "kay-net") is a deep learning framework implemented in Julia that allows the definition and training of machine learning models using the full power and expressivity of Julia. **Average Rating:** 4.3/5.0 **Total Reviews:** 12 **User Satisfaction Scores:** - **Ease of Use:** 8.9/10 (Category avg: 8.1/10) - **Quality of Support:** 9.0/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Knet](https://www.g2.com/sellers/knet) - **Year Founded:** 1990 - **HQ Location:** Kuwait, Kuwait - **Twitter:** @knet (68 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/the-shared-electronic-banking-services-co.-knet/about (232 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 42% Enterprise, 33% Mid-Market ### 7. [ConvNetJS](https://www.g2.com/products/convnetjs/reviews) ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in a browser. **Average Rating:** 3.8/5.0 **Total Reviews:** 13 **User Satisfaction Scores:** - **Ease of Use:** 9.3/10 (Category avg: 8.1/10) - **Quality of Support:** 8.0/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Stanford NLP Group](https://www.g2.com/sellers/stanford-nlp-group) - **HQ Location:** Stanford, CA - **Twitter:** @stanfordnlp (183,666 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 38% Enterprise, 38% Small-Business ### 8. [gobrain](https://www.g2.com/products/gobrain/reviews) gobrain is a neural networks written in go that includes just basic Neural Network functions such as Feed Forward and Elman Recurrent Neural Network. **Average Rating:** 4.5/5.0 **Total Reviews:** 11 **User Satisfaction Scores:** - **Ease of Use:** 8.6/10 (Category avg: 8.1/10) - **Quality of Support:** 8.9/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [gobrain](https://www.g2.com/sellers/gobrain) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 64% Small-Business, 36% Mid-Market ### 9. [NVIDIA Deep Learning GPU Training System (DIGITS)](https://www.g2.com/products/nvidia-deep-learning-gpu-training-system-digits/reviews) NVIDIA Deep Learning GPU Training System (DIGITS) deep learning for data science and research to quickly design deep neural network (DNN) for image classification and object detection tasks using real-time network behavior visualization. **Average Rating:** 4.5/5.0 **Total Reviews:** 22 **User Satisfaction Scores:** - **Ease of Use:** 8.3/10 (Category avg: 8.1/10) - **Quality of Support:** 7.8/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [NVIDIA](https://www.g2.com/sellers/nvidia) - **Year Founded:** 1993 - **HQ Location:** Santa Clara, CA - **Twitter:** @nvidia (2,479,137 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/3608/ (46,612 employees on LinkedIn®) - **Ownership:** NVDA **Reviewer Demographics:** - **Top Industries:** Computer Software - **Company Size:** 52% Small-Business, 35% Mid-Market ### 10. [Merlin](https://www.g2.com/products/merlin/reviews) Merlin is a deep learning framework written in Julia, it aims to provide a fast, flexible and compact deep learning library for machine learning. **Average Rating:** 3.6/5.0 **Total Reviews:** 10 **User Satisfaction Scores:** - **Ease of Use:** 8.9/10 (Category avg: 8.1/10) - **Quality of Support:** 6.4/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Merlin](https://www.g2.com/sellers/merlin) - **Year Founded:** 1993 - **HQ Location:** London, GB - **LinkedIn® Page:** https://www.linkedin.com/company/merlin_2 (427 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 50% Small-Business, 30% Mid-Market ### 11. [Google Cloud Deep Learning VM Image](https://www.g2.com/products/google-cloud-deep-learning-vm-image/reviews) Deep Learning VM Image Preconfigured VMs for deep learning applications. **Average Rating:** 4.3/5.0 **Total Reviews:** 13 **User Satisfaction Scores:** - **Ease of Use:** 8.3/10 (Category avg: 8.1/10) - **Quality of Support:** 7.6/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,885,216 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Reviewer Demographics:** - **Company Size:** 54% Small-Business, 38% Mid-Market ### 12. [node-fann](https://www.g2.com/products/node-fann/reviews) FANN (Fast Artificial Neural Network Library) is a free open source neural network library, which implements multilayer artificial neural networks with support for both fully connected and sparsely connected networks. **Average Rating:** 4.2/5.0 **Total Reviews:** 12 **User Satisfaction Scores:** - **Ease of Use:** 8.5/10 (Category avg: 8.1/10) - **Quality of Support:** 9.0/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [node-fann](https://www.g2.com/sellers/node-fann) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 50% Mid-Market, 42% Small-Business ### 13. [SuperLearner](https://www.g2.com/products/superlearner/reviews) SuperLearner is a package that implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner. **Average Rating:** 4.5/5.0 **Total Reviews:** 13 **User Satisfaction Scores:** - **Ease of Use:** 9.3/10 (Category avg: 8.1/10) - **Quality of Support:** 8.5/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Super Learner](https://www.g2.com/sellers/super-learner) - **Year Founded:** 2018 - **HQ Location:** Miami, US - **LinkedIn® Page:** https://www.linkedin.com/company/meet-super (1,281 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 38% Small-Business, 31% Enterprise ### 14. [Google Cloud Deep Learning Containers](https://www.g2.com/products/google-cloud-deep-learning-containers/reviews) Preconfigured and optimized containers for deep learning environments. **Average Rating:** 4.5/5.0 **Total Reviews:** 19 **User Satisfaction Scores:** - **Ease of Use:** 8.1/10 (Category avg: 8.1/10) - **Quality of Support:** 8.0/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Google](https://www.g2.com/sellers/google) - **Year Founded:** 1998 - **HQ Location:** Mountain View, CA - **Twitter:** @google (31,885,216 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®) - **Ownership:** NASDAQ:GOOG **Reviewer Demographics:** - **Company Size:** 45% Small-Business, 35% Enterprise ### 15. [Neuton AutoML](https://www.g2.com/products/neuton-automl/reviews) 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 Framework and AutoML solution currently available on the market. It allows users to evaluate the model quality from various perspectives and interpret prediction results. Neuton Explainability Office: - Exploratory Data Analysis - Feature Importance Matrix with class granularity - Model Interpreter - Feature Influence Matrix - Validate Model on New Data - Model-to-Data Relevance Indicators historical and for every prediction - Model Quality Index - Confidence Interval - Extensive list of supported metrics with Radar Diagram **Average Rating:** 4.5/5.0 **Total Reviews:** 17 **User Satisfaction Scores:** - **Ease of Use:** 9.1/10 (Category avg: 8.1/10) - **Quality of Support:** 8.5/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Bell Integrator](https://www.g2.com/sellers/bell-integrator) - **Year Founded:** 2003 - **HQ Location:** San Jose, CA - **LinkedIn® Page:** https://www.linkedin.com/company/bellintegrator/ (709 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 35% Enterprise, 35% Small-Business ### 16. [Torch](https://www.g2.com/products/torch/reviews) Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. **Average Rating:** 4.4/5.0 **Total Reviews:** 14 **User Satisfaction Scores:** - **Ease of Use:** 8.9/10 (Category avg: 8.1/10) - **Quality of Support:** 8.1/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Torch Leadership Labs](https://www.g2.com/sellers/torch-leadership-labs) - **Year Founded:** 2017 - **HQ Location:** San Francisco, US - **Twitter:** @torchlabs (3,069 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/torch-labs (379 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Computer Software - **Company Size:** 40% Small-Business, 40% Enterprise ### 17. [Swift AI](https://www.g2.com/products/swift-ai/reviews) Swift AI is a high-performance AI and machine learning library written entirely in Swift that includes a set of common tools used for machine learning and artificial intelligence research. **Average Rating:** 4.3/5.0 **Total Reviews:** 12 **User Satisfaction Scores:** - **Ease of Use:** 7.7/10 (Category avg: 8.1/10) - **Quality of Support:** 8.3/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Swift AI](https://www.g2.com/sellers/swift-ai) - **HQ Location:** Provo, UT - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Information Technology and Services - **Company Size:** 42% Enterprise, 33% Small-Business ### 18. [TFLearn](https://www.g2.com/products/tflearn/reviews) TFlearn is a modular and transparent deep learning library built on top of Tensorflow that provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. **Average Rating:** 4.0/5.0 **Total Reviews:** 20 **User Satisfaction Scores:** - **Ease of Use:** 8.9/10 (Category avg: 8.1/10) - **Quality of Support:** 6.9/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [TFLearn](https://www.g2.com/sellers/tflearn) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Computer Software, Information Technology and Services - **Company Size:** 55% Small-Business, 30% Enterprise ### 19. [H2O](https://www.g2.com/products/h2o/reviews) H2O.ai is the leading AI Cloud company, on a mission to democratize AI and drive an open AI movement around the world. They focus on drawing insights from structured and unstructured data like video and documents with their award-winning products like Hydrogen Torch and Document AI. Customers use the H2O AI Cloud to rapidly solve complex business problems and accelerate the discovery of new ideas. H2O.ai is the trusted AI provider to more than 20,000 global organizations, millions of data scientists and over half of the Fortune 500, including AT&T, Commonwealth Bank of Australia, Citi, GlaxoSmithKline, Hitachi, Kaiser Permanente, Procter & Gamble, PayPal, PwC, Reckitt, Unilever, Goldman Sachs, NVIDIA, and Wells Fargo are not only customers and partners, but strategic investors in the company. More than 30 Kaggle Grandmasters (the community of best-in-the-world machine learning practitioners and data scientists) are makers at H2O.ai. A strong AI for Good ethos to make the world a better place and Responsible AI drive the company’s purpose. Please join our movement at www.h2o.ai. H2O.ai offers enterprise customers with multiple platforms for AI and machine learning, including the open source distributed machine learning platform H2O-3, automatic machine learning platform H2O Driverless AI, and the recently announced H2O Q, an AI platform for business users: H2O-3 is an open source, scalable and distributed in-memory AI and machine learning platform. H2O-3 also has a strong AutoML functionality and supports the most widely used statistical and machine learning algorithms including gradient boosted machines, generalized linear models, deep learning, XGBoost and more. H2O Driverless AI empowers data scientists to work on projects faster and more efficiently by using automation to accomplish tasks quickly with automatic feature engineering, model tuning, model tuning, model selection, model validation and machine learning interpretability, custom recipes, time-series and automatic deployment pipeline generation for model scoring. H2O Q is a new AI platform that provides the essential building blocks to make AI apps and will bring the power of AI to millions of business users. It delivers automatic insights and predictions for “in the moment” business questions and is ideal for data analysts, citizen data scientists and all business users. **Average Rating:** 4.5/5.0 **Total Reviews:** 22 **User Satisfaction Scores:** - **Ease of Use:** 9.0/10 (Category avg: 8.1/10) - **Quality of Support:** 8.8/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [H2O.ai](https://www.g2.com/sellers/h2o-ai) - **Year Founded:** 2012 - **HQ Location:** Mountain View, CA - **Twitter:** @h2oai (25,274 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/2820918/ (335 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 54% Small-Business, 29% Enterprise ### 20. [Caffe](https://www.g2.com/products/caffe/reviews) Caffe is a deep learning framework made with expression, speed, and modularity in mind. **Average Rating:** 4.0/5.0 **Total Reviews:** 16 **User Satisfaction Scores:** - **Ease of Use:** 7.9/10 (Category avg: 8.1/10) - **Quality of Support:** 7.9/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Caffe](https://www.g2.com/sellers/caffe) - **Year Founded:** 2015 - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/caffe (691 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Computer Software - **Company Size:** 63% Small-Business, 19% Enterprise ### 21. [NVIDIA Deep Learning AMI](https://www.g2.com/products/nvidia-deep-learning-ami/reviews) NVIDIA Deep Learning AMI with Support by Terracloudx is a streamlined environment that enables you to run data science, HPC, and deep learning containers tuned specifically for GPUs. Terracloudx decision making is guided by the commitment and effort of our collaborators who continually work to be at the forefront of technology **Average Rating:** 4.5/5.0 **Total Reviews:** 10 **User Satisfaction Scores:** - **Ease of Use:** 8.9/10 (Category avg: 8.1/10) - **Quality of Support:** 9.3/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [terracloudx](https://www.g2.com/sellers/terracloudx) - **HQ Location:** N/A - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 70% Small-Business, 30% Enterprise ### 22. [DeepPy](https://www.g2.com/products/deeppy/reviews) DeepPy is a MIT licensed deep learning framework that tries to add a touch of zen to deep learning as it allows for Pythonic programming based on NumPy's ndarray,has a small and easily extensible codebase, runs on CPU or Nvidia GPUs and implements the following network architectures feedforward networks, convnets, siamese networks and autoencoders. **Average Rating:** 4.1/5.0 **Total Reviews:** 12 **User Satisfaction Scores:** - **Ease of Use:** 8.3/10 (Category avg: 8.1/10) - **Quality of Support:** 7.0/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [DeepPy](https://www.g2.com/sellers/deeppy) - **HQ Location:** N/A - **Twitter:** @deeppy (661 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Reviewer Demographics:** - **Top Industries:** Computer Software - **Company Size:** 67% Small-Business, 17% Enterprise ### 23. [Chainer](https://www.g2.com/products/chainer/reviews) Chainer is a powerful, flexible, and intuitive framework of neural networks that bridge the gap between algorithms and implementations. **Average Rating:** 4.3/5.0 **Total Reviews:** 11 **User Satisfaction Scores:** - **Ease of Use:** 7.9/10 (Category avg: 8.1/10) - **Quality of Support:** 7.7/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Chainer](https://www.g2.com/sellers/chainer) - **HQ Location:** Tokyo, Japan - **Twitter:** @ChainerOfficial - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 73% Small-Business, 18% Mid-Market ### 24. [Neuroph](https://www.g2.com/products/neuroph/reviews) Neuroph is lightweight Java neural network framework that develop common neural network architectures, it contains well designed, open source Java library with small number of basic classes which correspond to basic NN concepts and has s GUI neural network editor to quickly create Java neural network components. **Average Rating:** 4.6/5.0 **Total Reviews:** 6 **User Satisfaction Scores:** - **Ease of Use:** 9.2/10 (Category avg: 8.1/10) - **Quality of Support:** 6.7/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [Neuroph](https://www.g2.com/sellers/neuroph) - **HQ Location:** Belgrade - **Twitter:** @neuroph (369 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®) **Reviewer Demographics:** - **Company Size:** 67% Mid-Market, 17% Enterprise ### 25. [Fabric for Deep Learning (FfDL)](https://www.g2.com/products/fabric-for-deep-learning-ffdl/reviews) Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have contributed to the popularity of deep learning by reducing the effort and skills needed to design, train, and use deep learning models. Fabric for Deep Learning (FfDL, pronounced ‚Äúfiddle‚Äù) provides a consistent way to run these deep-learning frameworks as a service on Kubernetes. **Average Rating:** 3.9/5.0 **Total Reviews:** 5 **User Satisfaction Scores:** - **Ease of Use:** 5.6/10 (Category avg: 8.1/10) - **Quality of Support:** 6.7/10 (Category avg: 8.0/10) **Seller Details:** - **Seller:** [IBM](https://www.g2.com/sellers/ibm) - **Year Founded:** 1911 - **HQ Location:** Armonk, NY - **Twitter:** @IBM (709,023 Twitter followers) - **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®) - **Ownership:** SWX:IBM **Reviewer Demographics:** - **Company Size:** 80% Small-Business, 20% Mid-Market ## Parent Category [Deep Learning Software](https://www.g2.com/categories/deep-learning) --- ## Buyer Guide ### 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.