# Best Artificial Neural Network Software - Page 2

  *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:** [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)

  
  ## What Are the Top-Rated Artificial Neural Network Software Products in 2026?
### 1. [Swift Brain](https://www.g2.com/products/swift-brain/reviews)
  Swift Brain is a neural network / machine learning library written in Swift for AI algorithms in Swift for iOS and OS X development it includes algorithms focused on Bayes theorem, neural networks, SVMs, Matrices, etc.


  **Average Rating:** 3.8/5.0
  **Total Reviews:** 5
**How Do G2 Users Rate Swift Brain?**

- **Ease of Use:** 7.1/10 (Category avg: 8.1/10)
- **Quality of Support:** 7.1/10 (Category avg: 8.0/10)

**Who Is the Company Behind Swift Brain?**

- **Seller:** [Swift Brain](https://www.g2.com/sellers/swift-brain)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 60% Small-Business, 20% Enterprise


### 2. [Automaton AI](https://www.g2.com/products/automaton-ai/reviews)
  Automaton AI is an AI software company that provides platforms for Computer Vision & ML Scientists to rapidly curate and experiment with their datasets in order to build higher performing ML & DL models.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 14
**How Do G2 Users Rate Automaton AI?**

- **Ease of Use:** 9.1/10 (Category avg: 8.1/10)
- **Quality of Support:** 8.5/10 (Category avg: 8.0/10)

**Who Is the Company Behind Automaton AI?**

- **Seller:** [Automaton AI](https://www.g2.com/sellers/automaton-ai)
- **Year Founded:** 2019
- **HQ Location:** Pune, IN
- **Twitter:** @automatonai (16 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/automaton-ai-infosystem-pvt-ltd (50 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 50% Enterprise, 36% Small-Business


### 3. [Caffe Python](https://www.g2.com/products/caffe-python/reviews)
  Jetware is an automation tool to configure and manage server applications, such as databases, web servers, application servers, popular web applications such as Wordpress, Drupal, Redmine, and Confluence, or your own created applications. Jetware includes a runtime environment manager, a software applications collection, and a runtime environment constructor (online service and a command line utility). The online services and the package collections are provided free of charge.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 3
**How Do G2 Users Rate Caffe Python?**

- **Ease of Use:** 7.8/10 (Category avg: 8.1/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.0/10)

**Who Is the Company Behind Caffe Python?**

- **Seller:** [Jetware](https://www.g2.com/sellers/jetware-c6839872-6292-4a7b-973d-ac6da2ceaa45)
- **Year Founded:** 2017
- **HQ Location:** Roma, IT
- **Twitter:** @jetware_io (25 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/jetware.org/about/ (2 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 67% Small-Business, 33% Mid-Market


### 4. [Darknet](https://www.g2.com/products/darknet/reviews)
  Darknet is an open source neural network framework written in C and CUDA that supports CPU and GPU computation.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 3
**How Do G2 Users Rate Darknet?**

- **Ease of Use:** 10.0/10 (Category avg: 8.1/10)
- **Quality of Support:** 9.2/10 (Category avg: 8.0/10)

**Who Is the Company Behind Darknet?**

- **Seller:** [Darknet](https://www.g2.com/sellers/darknet)
- **HQ Location:** Vancouver, Canada
- **Twitter:** @pjreddie (14,788 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% Small-Business, 33% Enterprise


### 5. [Fido](https://www.g2.com/products/fido/reviews)
  Fido is a light-weight, open-source, and highly modular C++ machine learning library that targeted towards embedded electronics and robotics, it includes implementations of trainable neural networks, reinforcement learning methods, genetic algorithms, and a full-fledged robotic simulator.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 5
**How Do G2 Users Rate Fido?**

- **Ease of Use:** 10.0/10 (Category avg: 8.1/10)
- **Quality of Support:** 10.0/10 (Category avg: 8.0/10)

**Who Is the Company Behind Fido?**

- **Seller:** [The Fido Project](https://www.g2.com/sellers/the-fido-project)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 40% Enterprise, 20% Mid-Market


### 6. [MLKit](https://www.g2.com/products/mlkit/reviews)
  MLKit is a machine learning framework written in Swift that features machine learning algorithms that deal with the topic of regression to provide developers with a toolkit to create products that can learn from data.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 12
**How Do G2 Users Rate MLKit?**

- **Ease of Use:** 9.3/10 (Category avg: 8.1/10)
- **Quality of Support:** 9.2/10 (Category avg: 8.0/10)

**Who Is the Company Behind MLKit?**

- **Seller:** [MLKit](https://www.g2.com/sellers/mlkit)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 46% Small-Business, 31% Mid-Market


### 7. [BrainCore](https://www.g2.com/products/braincore/reviews)
  BrainCore is a neural network framework written in Swift that uses Metal which makes it fast.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 2
**How Do G2 Users Rate BrainCore?**

- **Ease of Use:** 6.7/10 (Category avg: 8.1/10)
- **Quality of Support:** 10.0/10 (Category avg: 8.0/10)

**Who Is the Company Behind BrainCore?**

- **Seller:** [BrainCore](https://www.g2.com/sellers/braincore)
- **HQ Location:** Hilton Head Island, SC
- **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


### 8. [Neurolab](https://www.g2.com/products/neurolab/reviews)
  Neurolab is a simple and powerful Neural Network Library for Python that contains based neural networks, train algorithms and flexible framework to create and explore other neural network types.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 2

**Who Is the Company Behind Neurolab?**

- **Seller:** [Neurolab](https://www.g2.com/sellers/neurolab)
- **HQ Location:** Asheville, NC
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 150% Small-Business


### 9. [Open Neural Network Exchange (ONNX)](https://www.g2.com/products/open-neural-network-exchange-onnx/reviews)
  ONNX is an open format built to represent machine learning models. ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers


  **Average Rating:** 4.0/5.0
  **Total Reviews:** 2
**How Do G2 Users Rate Open Neural Network Exchange (ONNX)?**

- **Ease of Use:** 7.5/10 (Category avg: 8.1/10)
- **Quality of Support:** 6.7/10 (Category avg: 8.0/10)

**Who Is the Company Behind Open Neural Network Exchange (ONNX)?**

- **Seller:** [The Linux Foundation](https://www.g2.com/sellers/the-linux-foundation)
- **Year Founded:** 2015
- **HQ Location:** San Francisco, CA
- **Twitter:** @hyperledger (294 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10851358/ (92 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 50% Mid-Market, 50% Enterprise


### 10. [RustNN](https://www.g2.com/products/rustnn/reviews)
  RustNN is a feedforward neural network library that generates fully connected multi-layer artificial neural networks that are trained via backpropagation.


  **Average Rating:** 3.3/5.0
  **Total Reviews:** 2
**How Do G2 Users Rate RustNN?**

- **Ease of Use:** 5.8/10 (Category avg: 8.1/10)
- **Quality of Support:** 6.7/10 (Category avg: 8.0/10)

**Who Is the Company Behind RustNN?**

- **Seller:** [RustNN](https://www.g2.com/sellers/rustnn)
- **HQ Location:** N/A
- **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% Mid-Market, 50% Small-Business


### 11. [SwiftLearner](https://www.g2.com/products/swiftlearner/reviews)
  SwiftLearner is a scala machine learning library that is easier to follow than the optimized libraries, and easier to tweak it use plain Java types and have few or no dependencies.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 3
**How Do G2 Users Rate SwiftLearner?**

- **Ease of Use:** 7.5/10 (Category avg: 8.1/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.0/10)

**Who Is the Company Behind SwiftLearner?**

- **Seller:** [SwiftLearner](https://www.g2.com/sellers/swiftlearner)
- **HQ Location:** N/A
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 67% Mid-Market, 33% Enterprise


### 12. [Ultralytics](https://www.g2.com/products/ultralytics/reviews)
  Ultralytics is a prominent player in the field of vision AI, specializing in advanced computer vision solutions through its innovative YOLO (You Only Look Once) models. Designed to assist users in various industries, Ultralytics' technology enables real-time object detection and image analysis, making it an essential tool for businesses looking to leverage artificial intelligence for enhanced operational efficiency and decision-making. Targeted at a diverse audience that includes professionals in manufacturing, healthcare, transportation, agriculture, and retail, Ultralytics' offerings cater to organizations seeking to implement AI-driven solutions. The versatility of the YOLO models allows users to address a wide range of use cases, from automating quality control in manufacturing to improving patient outcomes in healthcare settings. By providing accessible and efficient AI tools, Ultralytics empowers businesses to harness the power of computer vision, ultimately driving innovation and growth. Key features of Ultralytics' technology include its remarkable speed and accuracy in image processing, which allows for the analysis of 1.6 billion images daily. This capability is complemented by the ability to train 5 million models per day, ensuring that users have access to the most up-to-date and effective AI tools. The YOLO models are designed to be user-friendly, enabling users with varying levels of technical expertise to implement and benefit from the technology without extensive training or resources. The unique selling points of Ultralytics lie in its commitment to AI accessibility and efficiency. By providing open-source solutions with extensive community support, the company fosters collaboration and innovation within the AI space. The impressive track record of over 110,000 GitHub stars and more than 100 million downloads highlights the widespread adoption and trust in Ultralytics' models. As industries continue to evolve and embrace digital transformation, Ultralytics remains at the forefront, offering cutting-edge solutions that meet the demands of a rapidly changing technological landscape.


  **Average Rating:** 5.0/5.0
  **Total Reviews:** 2
**How Do G2 Users Rate Ultralytics?**

- **Ease of Use:** 10.0/10 (Category avg: 8.1/10)
- **Quality of Support:** 9.2/10 (Category avg: 8.0/10)

**Who Is the Company Behind Ultralytics?**

- **Seller:** [Ultralytics](https://www.g2.com/sellers/ultralytics)
- **Company Website:** https://ultralytics.com
- **Year Founded:** 2022
- **HQ Location:** 5001 Judicial Way Frederick, MD 21703, USA
- **Twitter:** @ultralytics (8,470 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/ultralytics (37 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 100% Mid-Market


#### What Are Ultralytics's Pros and Cons?

**Pros:**

- Deployment Ease (2 reviews)
- Ease of Use (2 reviews)
- Efficiency (2 reviews)
- AI Technology (1 reviews)
- Automation (1 reviews)

**Cons:**

- Poor Documentation (2 reviews)
- AI Limitations (1 reviews)
- Confusing Documentation (1 reviews)
- Deployment Issues (1 reviews)
- Insufficient Learning Resources (1 reviews)

### 13. [AForge.NET](https://www.g2.com/products/aforge-net/reviews)
  AForge.MachineLearning is a namespace that contains interfaces and classes for different algorithms of machine learning.


  **Average Rating:** 3.8/5.0
  **Total Reviews:** 2
**How Do G2 Users Rate AForge.NET?**

- **Ease of Use:** 6.7/10 (Category avg: 8.1/10)
- **Quality of Support:** 7.5/10 (Category avg: 8.0/10)

**Who Is the Company Behind AForge.NET?**

- **Seller:** [Accord.NET](https://www.g2.com/sellers/accord-net)
- **HQ Location:** N/A
- **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


### 14. [BrainChip](https://www.g2.com/products/brainchip/reviews)
  REVOLUTIONIZING ARTIFICIAL INTELLIGENCE AT THE EDGE


  **Average Rating:** 5.0/5.0
  **Total Reviews:** 1
**How Do G2 Users Rate BrainChip?**

- **Ease of Use:** 8.3/10 (Category avg: 8.1/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.0/10)

**Who Is the Company Behind BrainChip?**

- **Seller:** [BrainChip](https://www.g2.com/sellers/brainchip)
- **Year Founded:** 2013
- **HQ Location:** Laguna Hills, US
- **LinkedIn® Page:** https://www.linkedin.com/company/brainchip-holdings-limited/ (66 employees on LinkedIn®)
- **Ownership:** ASX: BRN

**Who Uses This Product?**
  - **Company Size:** 100% Enterprise


### 15. [DeepCube](https://www.g2.com/products/deepcube-deepcube/reviews)
  Nano Dimension (Nasdaq: NNDM) is a provider of intelligent machines for the fabrication of Additively Manufactured Electronics (AME). High fidelity active electronic and electromechanical subassemblies are integral enablers of autonomous intelligent drones, cars, satellites, smartphones, and in vivo medical devices. They necessitate iterative development, IP safety, fast time-to-market and device performance gains, thereby mandating AME for in-house, rapid prototyping and production. Nano Dimension machines serve cross-industry needs by depositing proprietary consumable conductive and dielectric materials simultaneously, while concurrently integrating in-situ capacitors, antennas, coils, transformers and electromechanical components, to function at unprecedented performance. Nano Dimension bridges the gap between PCB and semiconductor Integrated Circuits. A revolution at the click of a button: From CAD to a functional high-performance AME device in hours, solely at the cost of the consumable materials.


  **Average Rating:** 5.0/5.0
  **Total Reviews:** 1
**How Do G2 Users Rate DeepCube?**

- **Ease of Use:** 5.0/10 (Category avg: 8.1/10)
- **Quality of Support:** 6.7/10 (Category avg: 8.0/10)

**Who Is the Company Behind DeepCube?**

- **Seller:** [DeepCube](https://www.g2.com/sellers/deepcube)
- **HQ Location:** N/A
- **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% Small-Business, 100% Enterprise


### 16. [Deep Java Library (DJL)](https://www.g2.com/products/deep-java-library-djl/reviews)
  Deep Java Library is an open-source, high-level, engine-agnostic Java framework for deep learning. Designed to provide a native Java development experience, DJL enables developers to build, train, and deploy deep learning models using familiar Java tools and IDEs. Its intuitive API abstracts the complexities of deep learning, allowing seamless integration into Java applications without requiring extensive machine learning expertise. DJL supports multiple deep learning engines, including Apache MXNet, PyTorch, and TensorFlow, offering flexibility and adaptability to various project requirements. Key Features and Functionality: - Engine Agnostic: Developers can write code once and run it on different deep learning engines without modification, facilitating flexibility and future-proofing applications. - Native Java API: DJL offers intuitive APIs that align with native Java concepts, simplifying the development process for Java programmers. - Model Zoo: Access a repository of pre-trained models, enabling quick integration of state-of-the-art AI capabilities into Java applications. - Ease of Deployment: DJL simplifies the deployment of deep learning models, allowing developers to bring in their own models or use existing ones from the Model Zoo, facilitating rapid deployment in production environments. - Hardware Optimization: The library automatically selects between CPU and GPU based on available hardware, ensuring optimal performance without manual configuration. Primary Value and Problem Solved: DJL addresses the gap in deep learning tools for Java developers by providing a comprehensive, easy-to-use framework that integrates seamlessly with existing Java applications. It eliminates the need for developers to switch to other programming languages to implement deep learning solutions, thereby reducing development time and complexity. By supporting multiple deep learning engines and offering a rich set of pre-trained models, DJL empowers Java developers to incorporate advanced AI capabilities into their applications efficiently.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 1
**How Do G2 Users Rate Deep Java Library (DJL)?**

- **Ease of Use:** 8.3/10 (Category avg: 8.1/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.0/10)

**Who Is the Company Behind Deep Java Library (DJL)?**

- **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 (2,227,557 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Who Uses This Product?**
  - **Company Size:** 100% Enterprise


### 17. [Exafunction](https://www.g2.com/products/exafunction/reviews)
  Exafunction optimizes your deep learning inference workload, delivering up to a 10x improvement in resource utilization and cost. Focus on building your deep learning application, not on managing clusters and fine-tuning performance.


  **Average Rating:** 4.0/5.0
  **Total Reviews:** 1
**How Do G2 Users Rate Exafunction?**

- **Ease of Use:** 6.7/10 (Category avg: 8.1/10)
- **Quality of Support:** 10.0/10 (Category avg: 8.0/10)

**Who Is the Company Behind Exafunction?**

- **Seller:** [Exafunction](https://www.g2.com/sellers/exafunction)
- **Year Founded:** 2021
- **HQ Location:** Mountain View, US
- **LinkedIn® Page:** https://www.linkedin.com/company/80796312 (1 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 100% Small-Business


### 18. [Horovod](https://www.g2.com/products/horovod/reviews)
  Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve.


  **Average Rating:** 3.0/5.0
  **Total Reviews:** 1
**How Do G2 Users Rate Horovod?**

- **Ease of Use:** 5.0/10 (Category avg: 8.1/10)
- **Quality of Support:** 5.0/10 (Category avg: 8.0/10)

**Who Is the Company Behind Horovod?**

- **Seller:** [The Linux Foundation](https://www.g2.com/sellers/the-linux-foundation)
- **Year Founded:** 2015
- **HQ Location:** San Francisco, CA
- **Twitter:** @hyperledger (294 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10851358/ (92 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 100% Small-Business


### 19. [MindsDB](https://www.g2.com/products/mindsdb/reviews)
  MindsDB is an AI data solution that enables humans, AI, agents, and applications to query data in natural language and SQL, and get highly accurate answers across disparate data sources and types. MindsDB connects to diverse data sources and applications, and unifies petabyte-scale structured and unstructured data. Powered by an industry-first cognitive engine that can operate anywhere (on-prem, VPC, serverless), it empowers both humans and AI with highly informed decision-making capabilities. MindsDB has two AI solutions, the Minds Enterprise and MindsDB Open Source. Our Value Pillars: - Connect to a wide range of data sources and applications using a single interface and language using the Federated query engine. - MindsDB's Knowledge Base unifies and makes sense of structured and unstructured data. - Minds "Cognition" understands, plans, finds, and retrieves the best data to respond to questions while offering full transparency of their thoughts and user actions to IT/operators. Making Enterprise Data Intelligent and Responsive for AI.


  **Average Rating:** 3.5/5.0
  **Total Reviews:** 1
**How Do G2 Users Rate MindsDB?**

- **Ease of Use:** 8.3/10 (Category avg: 8.1/10)
- **Quality of Support:** 5.0/10 (Category avg: 8.0/10)

**Who Is the Company Behind MindsDB?**

- **Seller:** [MindsDB](https://www.g2.com/sellers/mindsdb)
- **Year Founded:** 2017
- **HQ Location:** Berkeley, US
- **Twitter:** @MindsDB (77,767 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/mindsdb/ (47 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 100% Mid-Market


#### What Are MindsDB's Pros and Cons?

**Pros:**

- Coding Ease (1 reviews)
- Ease of Use (1 reviews)
- Machine Learning (1 reviews)
- Powerful (1 reviews)
- Predictive Modeling (1 reviews)

**Cons:**

- Learning Curve (1 reviews)
- Limited Customization (1 reviews)
- Required Knowledge (1 reviews)

### 20. [Mipsology](https://www.g2.com/products/mipsology/reviews)
  Zebra by Mipsology is the ideal Deep Learning compute engine for neural network inference. Zebra seamlessly replaces or complements CPUs/GPUs, allowing any neural network to compute faster, with lower power consumption, at lower cost. Zebra deploys swiftly, seamlessly and painlessly without knowledge of underlying hardware technology, use of specific compilation tools or to changes to the neural network, the training, the framework and the application.


  **Average Rating:** 5.0/5.0
  **Total Reviews:** 1
**How Do G2 Users Rate Mipsology?**

- **Ease of Use:** 10.0/10 (Category avg: 8.1/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.0/10)

**Who Is the Company Behind Mipsology?**

- **Seller:** [AMD](https://www.g2.com/sellers/amd)
- **Year Founded:** 1969
- **HQ Location:** Santa Clara, California
- **LinkedIn® Page:** https://www.linkedin.com/company/amd/ (62,932 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMD

**Who Uses This Product?**
  - **Company Size:** 100% Small-Business


### 21. [OmniML](https://www.g2.com/products/omniml/reviews)
  OmniML is an enterprise artificial intelligence (AI) company that aims to effortlessly empower AI everywhere.


  **Average Rating:** 3.0/5.0
  **Total Reviews:** 1
**How Do G2 Users Rate OmniML?**

- **Ease of Use:** 6.7/10 (Category avg: 8.1/10)
- **Quality of Support:** 6.7/10 (Category avg: 8.0/10)

**Who Is the Company Behind OmniML?**

- **Seller:** [OmniML](https://www.g2.com/sellers/omniml)
- **HQ Location:** San Jose, US
- **LinkedIn® Page:** https://www.linkedin.com/company/77138596 (2 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 100% Small-Business


### 22. [Strong Compute](https://www.g2.com/products/strong-compute/reviews)
  Easy and Blazing fast procurement, command and control for AI compute.


  **Average Rating:** 5.0/5.0
  **Total Reviews:** 1

**Who Is the Company Behind Strong Compute?**

- **Seller:** [Strong Compute](https://www.g2.com/sellers/strong-compute)
- **HQ Location:** Sydney, AU
- **LinkedIn® Page:** http://www.linkedin.com/company/strongcompute (13 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 100% Small-Business


### 23. [Zama](https://www.g2.com/products/zama/reviews)
  Build Applications with‍ Fully Homomorphic Encryption (FHE). Zama is an open source cryptography company building state-of-the-art FHE solutions for blockchain and AI.


  **Average Rating:** 4.0/5.0
  **Total Reviews:** 1
**How Do G2 Users Rate Zama?**

- **Ease of Use:** 5.0/10 (Category avg: 8.1/10)
- **Quality of Support:** 5.0/10 (Category avg: 8.0/10)

**Who Is the Company Behind Zama?**

- **Seller:** [Zama](https://www.g2.com/sellers/zama)
- **Year Founded:** 2020
- **HQ Location:** Paris, FR
- **LinkedIn® Page:** http://www.linkedin.com/company/zama-ai (158 employees on LinkedIn®)

**Who Uses This Product?**
  - **Company Size:** 100% Small-Business


### 24. [Accord.NET Framework](https://www.g2.com/products/accord-net-framework/reviews)
  Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#, it is a framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use


  **Average Rating:** 5.0/5.0
  **Total Reviews:** 1

**Who Is the Company Behind Accord.NET Framework?**

- **Seller:** [Accord.NET](https://www.g2.com/sellers/accord-net)
- **HQ Location:** N/A
- **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% Small-Business


### 25. [Aimotive](https://www.g2.com/products/aimotive/reviews)
  aiMotive is a leading automotive technology company specializing in automated driving solutions. Their integrated product portfolio includes aiData, aiSim, and aiWare, designed to enable OEMs to develop and deploy scalable automated driving features efficiently. By combining advanced data tools with embedded solutions, aiMotive helps reduce development costs and accelerate time-to-market for automotive players. Key Features and Functionality: - aiData: A comprehensive data pipeline that automates data collection, annotation, and synthetic training data generation, ensuring high-quality datasets for developing safe automated driving solutions. - aiSim: A virtual validation suite offering scalable, high-fidelity sensor and environment simulation for real-time testing, facilitating the validation of complex automated driving systems from concept to production. - aiWare: A high-performance neural processing unit (NPU) IP core delivering up to 98% efficiency for a wide range of automotive neural networks, designed for power-efficient, low-latency AI inference in automotive applications. Primary Value and User Solutions: aiMotive's solutions address the critical challenges in automated driving development by providing an end-to-end toolchain that enhances data processing, simulation, and AI inference capabilities. This holistic approach enables automotive manufacturers to bridge technology gaps, reduce development costs, and accelerate the deployment of advanced driver-assistance systems (ADAS) and autonomous driving features, ultimately contributing to safer and more efficient vehicles on the road.



**Who Is the Company Behind Aimotive?**

- **Seller:** [Aimotive](https://www.g2.com/sellers/aimotive)
- **Year Founded:** 2015
- **HQ Location:** Budapest, HU
- **LinkedIn® Page:** https://www.linkedin.com/company/aimotive (312 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.