# Best Artificial Neural Network Software

## How Many Artificial Neural Network Software Products Does G2 Track?

**Total Products under this Category:** 95

### Category Stats (Jul 2026)

- **Average Rating:** 4.28/5 The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** AWS Deep Learning AMIs (+0.59%) - Among all products in this category, AWS Deep Learning AMIs recorded the largest rating increase compared to last month

_Last updated: July 31, 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
- 95+ 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.

## G2 Grid® for Artificial Neural Network Software
 ![G2 Grid® for Artificial Neural Network Software plotting products by satisfaction and market presence](https://www.g2.com/categories/artificial-neural-network/grids.png?focus%5B%5D=113983&focus%5B%5D=40845&focus%5B%5D=1318188&focus%5B%5D=21757&focus%5B%5D=21499&focus%5B%5D=21666&focus%5B%5D=21613&focus%5B%5D=146295)

Highlighted products: Google Cloud Deep Learning Containers, AWS Deep Learning AMIs, Google Cloud Deep Learning VM Image, AIToolbox, Microsoft Cognitive Toolkit (Formerly CNTK), Keras, Knet, and PyTorch.

Underlying data: [Grid® JSON](https://www.g2.com/categories/artificial-neural-network/grids.json?focus%5B%5D=google-cloud-deep-learning-containers&focus%5B%5D=aws-deep-learning-amis&focus%5B%5D=google-cloud-deep-learning-vm-image&focus%5B%5D=aitoolbox&focus%5B%5D=microsoft-cognitive-toolkit-formerly-cntk&focus%5B%5D=keras&focus%5B%5D=knet&focus%5B%5D=pytorch)

**Sponsored**

### Gemini

Gemini is a family of multimodal, generative AI models. These models were developed by Google DeepMind and Google Research. They are designed to understand, operate across, and combine different types of information. This includes text, images, audio, video, and code. Gemini serves as a versatile, everyday AI assistant and powers a conversational chatbot. Key Product Features & Capabilities Multimodal Understanding: Gemini understands and combines text, images, audio, video, and code. It can analyze complex documents, code repositories, and long videos. Conversational AI: Gemini allows for natural conversations. It functions as an intelligent assistant that can brainstorm, plan, and discuss topics. Deep Research & Analysis: Gemini can analyze websites and user files to generate reports. It can also create audio overviews of the information. Agentic Capabilities: Users can create custom "Gems" (specialized AI experts). The models can act as agents to take actions in tools like Chrome. Integrated Productivity: Gemini is integrated into Gmail, Google Docs, Drive, and Meet. This helps summarize, write, edit, and organize information. Creative Tools: Features include image generation and video creation, enabling the generation of 8-second videos with sound. Long Context Window: High-end models feature up to a 1 million-token context window. This is capable of analyzing large amounts of data.

[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=686&secure%5Bchosen_at%5D=2026-07-31T17%3A08%3A29Z&secure%5Bdisplayable_resource_id%5D=686&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=686&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=1332376&secure%5Bresource_id%5D=686&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fartificial-neural-network%3Fopen_modal_url%3D%252Fproducts%252Fkeras%252Fwishlists%253Fhost_path%253D%25252Fcategories%25252Fartificial-neural-network%2526source%253Dcategory&secure%5Btoken%5D=5f9e7121e7cc97046e5eca3b7507c76b941c79862faecf2b131b72ac841fe7d6&secure%5Burl%5D=https%3A%2F%2Fdocs.cloud.google.com%2Fgemini-enterprise-agent-platform%2Fmodels%2Fgoogle-models%2F%3Futm_source%3DG2%26utm_medium%3Ddisplay%26utm_campaign%3DCloud-SS-DR-GCP-1713658-GCP-DR-NA-US-en-G2-Display-Banner-All-%25epid%21-%25ecid%21-gemini%26utm_content%3D%257Bdevice%257D-%257Badgroupid%257D-%257Bnetwork%257D-%257Btargetid%257D-%257Bloc_physical_ms%257D-%257Bcampaignid%257D&secure%5Burl_type%5D=custom_url)

### [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:** 4.4/5.0

**Total Reviews:** 28

#### How Do G2 Users Rate Google Cloud Deep Learning Containers?

- **Ease of Use:** 8.4/10 (Category avg: 8.1/10)
- **Quality of Support:** 8.1/10 (Category avg: 8.0/10)

#### Who Is the Company Behind Google Cloud Deep Learning Containers?

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google  
31,899,995 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website)  
341,888 employees on LinkedIn®
- **Ownership:** NASDAQ:GOOG

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services
- **Company Size:** 35% Medium, 32% Large

#### What Are Recent G2 Reviews of Google Cloud Deep Learning Containers?

**["A Powerful Internal ML Asset Catalog That Made Sharing Notebooks and Pipelines Effortless"](https://www.g2.com/survey_responses/google-cloud-deep-learning-containers-review-13166573)**

**Rating:** 4.0/5.0 stars

_— Luca P._

[Read full review](https://www.g2.com/survey_responses/google-cloud-deep-learning-containers-review-13166573)

**["Saves Setup Time with Preinstalled PyTorch and Smooth Team Collaboration"](https://www.g2.com/survey_responses/google-cloud-deep-learning-containers-review-13175554)**

**Rating:** 5.0/5.0 stars

_— Javier C._

[Read full review](https://www.g2.com/survey_responses/google-cloud-deep-learning-containers-review-13175554)

#### What Are G2 Users Discussing About Google Cloud Deep Learning Containers?

- [What is Google Cloud Deep Learning Containers used for?](https://www.g2.com/discussions/what-is-google-cloud-deep-learning-containers-used-for) - 1 upvote

### [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:** 4.4/5.0

**Total Reviews:** 24

#### How Do G2 Users Rate AWS Deep Learning AMIs?

- **Ease of Use:** 9.1/10 (Category avg: 8.1/10)
- **Quality of Support:** 8.8/10 (Category avg: 8.0/10)

#### Who Is the Company Behind AWS Deep Learning AMIs?

- **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  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

#### Who Uses This Product?

- **Top Industries:** Computer Software
- **Company Size:** 46% Large, 29% Medium

#### What Are Recent G2 Reviews of AWS Deep Learning AMIs?

**["AWS Deep Learning AMIs: Fast, Pre-Configured AI/ML Setup That Boosts Productivity"](https://www.g2.com/survey_responses/aws-deep-learning-amis-review-12838360)**

**Rating:** 5.0/5.0 stars

_— Govind J._

[Read full review](https://www.g2.com/survey_responses/aws-deep-learning-amis-review-12838360)

**["Pre-Configured, Optimized AI Environment with AWS Deep Learning AMI"](https://www.g2.com/survey_responses/aws-deep-learning-amis-review-12883890)**

**Rating:** 4.5/5.0 stars

_— Prabhat C._

[Read full review](https://www.g2.com/survey_responses/aws-deep-learning-amis-review-12883890)

### [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:** 19

#### How Do G2 Users Rate Google Cloud Deep Learning VM Image?

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

#### Who Is the Company Behind Google Cloud Deep Learning VM Image?

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google  
31,899,995 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website)  
341,888 employees on LinkedIn®
- **Ownership:** NASDAQ:GOOG

#### Who Uses This Product?

- **Company Size:** 48% Small, 38% Medium

#### What Are Recent G2 Reviews of Google Cloud Deep Learning VM Image?

**["Preconfigured Deep Learning VM Image Speeds Up GPU-Accelerated AI Work on Google Cloud"](https://www.g2.com/survey_responses/google-cloud-deep-learning-vm-image-review-13186953)**

**Rating:** 4.5/5.0 stars

_— Subhashree S._

[Read full review](https://www.g2.com/survey_responses/google-cloud-deep-learning-vm-image-review-13186953)

**["Deep Learning Win"](https://www.g2.com/survey_responses/google-cloud-deep-learning-vm-image-review-13180464)**

**Rating:** 5.0/5.0 stars

_— Nia S._

[Read full review](https://www.g2.com/survey_responses/google-cloud-deep-learning-vm-image-review-13180464)

### [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:** 35

#### How Do G2 Users Rate AIToolbox?

- **Ease of Use:** 8.8/10 (Category avg: 8.1/10)
- **Quality of Support:** 8.9/10 (Category avg: 8.0/10)

#### Who Is the Company Behind AIToolbox?

- **Seller:** [AIToolbox](https://www.g2.com/sellers/aitoolbox)
- **HQ Location:** N/A
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7886df2ed926834e5eb248c77dcfa8e5c815d3ab1f0fe3132ced0dba45868834&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2FNo-Linkedin-Presence-Added-Intentionally-By-DataOps&secure%5Burl_type%5D=linkedin_company_website)  
1 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 54% Small, 40% Medium

#### What Do G2 Reviewers Say About AIToolbox?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of AIToolbox, finding it convenient to access various AI tools all in one place.
- Users appreciate the **model variety** of AIToolbox, making it easy to find and use essential AI tools.
- Users value the **comprehensive AI coverage** of AIToolbox, enhancing convenience and efficiency in development projects.
- Users appreciate the **easy integrations** with various AI modules, enhancing development speed and convenience in implementation.
- Users appreciate the **wide range of AI tools** in AIToolbox, making experimentation and application easy and efficient.

##### Cons

- Users struggle with the **inaccuracy** of AIToolbox, facing issues like unexplained flags and hallucinatory logic during analyses.
- Users note the **limited features** of AIToolbox, often finding them basic and lacking advanced customization options.
- Users are frustrated with the **lack of transparency and frequent errors** in AIToolbox's AI functionality during transactions.
- Users report **compatibility issues** with the MINOR version, causing disruptions in production and issues with high-frequency database operations.
- Users find the **complex setup** of AIToolbox challenging, suggesting improvements for easier integration and workflow efficiency.

#### What Are Recent G2 Reviews of AIToolbox?

**["Generous Free Tier and Easy AI-Powered Social Post Creation"](https://www.g2.com/survey_responses/aitoolbox-review-12213473)**

**Rating:** 5.0/5.0 stars

_— Tony P._

[Read full review](https://www.g2.com/survey_responses/aitoolbox-review-12213473)

**["Flexible Integration and Powerful Automation in One Platform"](https://www.g2.com/survey_responses/aitoolbox-review-12473904)**

**Rating:** 4.5/5.0 stars

_— Alexis V._

[Read full review](https://www.g2.com/survey_responses/aitoolbox-review-12473904)

#### What Are G2 Users Discussing About AIToolbox?

- [What is AIToolbox used for?](https://www.g2.com/discussions/what-is-aitoolbox-used-for) - 1 comment

## FAQs About Artificial Neural Network Software

Generated using AI

Last updated: June 3, 2026

### Artificial Neural Network tools offering flexible training workflows and straightforward conversion to optimized inference formats

According to verified users, buyers looking for flexible training workflows often value preconfigured environments, support for common frameworks, and fewer setup steps before experimentation begins. Reviews also point to the importance of smooth conversion or export into optimized inference formats for deployment on edge devices, mobile apps, or other production targets. Users mention that strong options reduce dependency conflicts, simplify dataset-to-model iteration, and help teams move from prototype to inference faster. Common review themes also include GPU readiness, Python-based development, and support for formats or runtimes that make deployment more efficient without requiring extensive manual tuning.

### Neural Network libraries with proven object detection and visual recognition capabilities in production logistics environments

According to verified users, object detection and visual recognition use cases in logistics center on dependable model training, real-time inference, and deployment in operational environments. Reviews describe teams using neural network tools to detect cases on pallets, support robotic picking, and enable computer vision workflows that connect training to production. Buyers may want to look for tools that support custom datasets, optimized export formats, and efficient execution on edge hardware. Review themes also highlight the value of high-level APIs, easier experimentation, and deployment paths that reduce the complexity of converting models for practical industrial vision applications.

### Neural Network platforms with easy-to-use Python APIs for rapid prototyping of convolutional models across vision tasks

According to verified users, easy-to-use Python APIs matter because they shorten the time between an idea and a working vision model. Reviews repeatedly emphasize rapid prototyping, simpler debugging, and the ability to experiment without heavy configuration. Buyers evaluating these platforms may want to focus on how easily teams can build, test, and iterate on convolutional or other vision models using familiar Python workflows. Review content also points to the benefit of preinstalled frameworks, intuitive interfaces, and support for GPU-backed experimentation. At the same time, some users mention setup complexity, compatibility issues, or production deployment tradeoffs once projects move beyond prototyping.

### What are the most important features in neural network software

G2 reviewers mention that the most important features in neural network software are usually fast environment setup, support for popular frameworks, GPU acceleration, and flexible experimentation workflows. Buyers also appear to value Python-friendly development, preconfigured dependencies, and tools that reduce configuration friction during training. For production use, reviews point to export and inference options, easier deployment to cloud or edge environments, and enough customization to match project requirements. Documentation quality and debugging support also come up often. In practice, users favor software that helps them move quickly from setup to model training while keeping deployment, iteration, and maintenance manageable for technical teams.

### How does Artificial Neural Network integrate with Python

G2 reviewers mention Python as a primary way teams interact with artificial neural network tools, especially for prototyping, model training, and experimentation. Recent reviews highlight Python-ready environments with preinstalled libraries, SDK support, and APIs that make it easier to build workflows without extensive manual setup. Buyers may find that Python integration is strongest when frameworks, drivers, and dependencies are already configured, since that reduces compatibility issues and speeds up testing. Review themes also connect Python workflows to database querying, model experimentation, and vision tasks. In general, users value products that let developers stay in familiar Python-based workflows from initial testing through inference preparation.

### [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

#### How Do G2 Users Rate Microsoft Cognitive Toolkit (Formerly CNTK)?

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

#### Who Is the Company Behind Microsoft Cognitive Toolkit (Formerly CNTK)?

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9458f51bd6ded48ad432a804f19ad736469f007787569b63827154231c315630&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmicrosoft%2F&secure%5Burl_type%5D=linkedin_company_website)  
231,632 employees on LinkedIn®
- **Ownership:** MSFT

#### Who Uses This Product?

- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 68% Large, 27% Small

#### What Do G2 Reviewers Say About Microsoft Cognitive Toolkit (Formerly CNTK)?

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **efficient workflow** of Microsoft Cognitive Toolkit, enhancing their productivity and project management.

##### Cons

- Users find Microsoft Cognitive Toolkit **overwhelming due to its complexity** , which can hinder their effective usage.
- Users find the **learning curve overwhelming** , making it challenging to effectively use Microsoft Cognitive Toolkit.

#### What Are Recent G2 Reviews of Microsoft Cognitive Toolkit (Formerly CNTK)?

**["It was a good product. Easy to use and support was helpful"](https://www.g2.com/survey_responses/microsoft-cognitive-toolkit-formerly-cntk-review-10277326)**

**Rating:** 4.0/5.0 stars

_— m p._

[Read full review](https://www.g2.com/survey_responses/microsoft-cognitive-toolkit-formerly-cntk-review-10277326)

**["Review Microsoft Cognitive Toolkit"](https://www.g2.com/survey_responses/microsoft-cognitive-toolkit-formerly-cntk-review-7182565)**

**Rating:** 4.5/5.0 stars

_— Rajneesh K._

[Read full review](https://www.g2.com/survey_responses/microsoft-cognitive-toolkit-formerly-cntk-review-7182565)

#### What Are G2 Users Discussing About Microsoft Cognitive Toolkit (Formerly CNTK)?

- [Is CNTK a deep learning framework?](https://www.g2.com/discussions/is-cntk-a-deep-learning-framework)
- [Is CNTK dead?](https://www.g2.com/discussions/is-cntk-dead) - 1 comment
- [Is Microsoft Cognitive Toolkit open source?](https://www.g2.com/discussions/is-microsoft-cognitive-toolkit-open-source)
- [What is CNTK used for?](https://www.g2.com/discussions/what-is-cntk-used-for)

### [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

#### How Do G2 Users Rate Keras?

- **Ease of Use:** 8.9/10 (Category avg: 8.1/10)
- **Quality of Support:** 7.8/10 (Category avg: 8.0/10)

#### Who Is the Company Behind Keras?

- **Seller:** [Keras](https://www.g2.com/sellers/keras)
- **Year Founded:** 2016
- **HQ Location:** N/A
- **Twitter:** @keras  
26 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=e472f10d811afa81f6c91174429c2d0cf2a090ff5af0e8eeea35ce0e1b13b51e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fkeras%2F&secure%5Burl_type%5D=linkedin_company_website)  
24 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Scientist
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 38% Small, 32% Medium

#### What Are Recent G2 Reviews of Keras?

**["A Versatile Tool for Machine Learning, Computer Vision, and Deep Learning"](https://www.g2.com/survey_responses/keras-review-12598879)**

**Rating:** 4.0/5.0 stars

_— Verified User in Renewables & Environment_

[Read full review](https://www.g2.com/survey_responses/keras-review-12598879)

**["Best DL Framework"](https://www.g2.com/survey_responses/keras-review-8604851)**

**Rating:** 5.0/5.0 stars

_— Aakash Kumar A._

[Read full review](https://www.g2.com/survey_responses/keras-review-8604851)

#### What Are G2 Users Discussing About Keras?

- [Why we use keras in machine learning?](https://www.g2.com/discussions/why-we-use-keras-in-machine-learning)
- [What is keras and why it is used?](https://www.g2.com/discussions/what-is-keras-and-why-it-is-used)
- [What is the purpose of keras?](https://www.g2.com/discussions/what-is-the-purpose-of-keras)
- [What are the features of keras?](https://www.g2.com/discussions/what-are-the-features-of-keras)

### [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:** 22

#### How Do G2 Users Rate PyTorch?

- **Ease of Use:** 8.7/10 (Category avg: 8.1/10)
- **Quality of Support:** 8.1/10 (Category avg: 8.0/10)

#### Who Is the Company Behind PyTorch?

- **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:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=776dc0660ec4fdc63006d90be47aaeb7018753ef0374aaafb08e80dd0af0063f&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fjetware.org%2Fabout%2F&secure%5Burl_type%5D=linkedin_company_website)  
2 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computer Software
- **Company Size:** 43% Medium, 43% Small

#### What Do G2 Reviewers Say About PyTorch?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **intuitive nature** of PyTorch, enjoying easy experimentation and debugging for their projects.
- Users value the **extensive documentation** of PyTorch, which enhances their learning and development experience significantly.
- Users find PyTorch's **intuitive interface** enhances their experience, facilitating easy experimentation and debugging for developers.
- Users love the **intuitive design** of PyTorch, making experimentation and debugging straightforward and efficient.
- Users value the **intuitive problem-solving capabilities** of PyTorch, enhancing experimentation and debugging for developers.

##### Cons

- Users find the **complexity** of deploying models in PyTorch can hinder smooth production workflows, necessitating extra tools.
- Users find that **difficult learning** curves for advanced features in PyTorch hinder smoother model deployment and scaling.
- Users find **difficult navigation** in PyTorch due to the need for extensive setup and advanced configuration for deployment.

#### What Are Recent G2 Reviews of PyTorch?

**["Flexible and Intuitive Deep Learning Framework"](https://www.g2.com/survey_responses/pytorch-review-11698021)**

**Rating:** 4.5/5.0 stars

_— Jagdish P._

[Read full review](https://www.g2.com/survey_responses/pytorch-review-11698021)

**["Powerful Open-Source Python Library for Machine Learning"](https://www.g2.com/survey_responses/pytorch-review-13093960)**

**Rating:** 4.5/5.0 stars

_— Verified User in Information Technology and Services_

[Read full review](https://www.g2.com/survey_responses/pytorch-review-13093960)

#### What Are G2 Users Discussing About PyTorch?

- [How do you extract features in PyTorch?](https://www.g2.com/discussions/how-do-you-extract-features-in-pytorch)
- [What are the advantages of PyTorch?](https://www.g2.com/discussions/what-are-the-advantages-of-pytorch)
- [What makes PyTorch special?](https://www.g2.com/discussions/what-makes-pytorch-special)
- [What can you do with PyTorch?](https://www.g2.com/discussions/what-can-you-do-with-pytorch) - 1 comment

### [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

#### How Do G2 Users Rate Knet?

- **Ease of Use:** 8.9/10 (Category avg: 8.1/10)
- **Quality of Support:** 9.0/10 (Category avg: 8.0/10)

#### Who Is the Company Behind Knet?

- **Seller:** [Knet](https://www.g2.com/sellers/knet)
- **Year Founded:** 1990
- **HQ Location:** Kuwait, Kuwait
- **Twitter:** @knet  
68 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=23ca7c5321768af3fc1359100b1620454a0c7aef35fe05a4a0e9161df68628a7&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fthe-shared-electronic-banking-services-co.-knet%2Fabout&secure%5Burl_type%5D=linkedin_company_website)  
240 employees on LinkedIn®

#### Who Uses This Product?

- **Company Size:** 42% Large, 33% Medium

#### What Are Recent G2 Reviews of Knet?

**["Knet the great"](https://www.g2.com/survey_responses/knet-review-7062700)**

**Rating:** 5.0/5.0 stars

_— Verified User in Computer Software_

[Read full review](https://www.g2.com/survey_responses/knet-review-7062700)

**["The best place to learn more about everything."](https://www.g2.com/survey_responses/knet-review-6811665)**

**Rating:** 5.0/5.0 stars

_— Eesha J._

[Read full review](https://www.g2.com/survey_responses/knet-review-6811665)

### [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

#### How Do G2 Users Rate Merlin?

- **Ease of Use:** 8.9/10 (Category avg: 8.1/10)
- **Quality of Support:** 6.4/10 (Category avg: 8.0/10)

#### Who Is the Company Behind Merlin?

- **Seller:** [Merlin](https://www.g2.com/sellers/merlin)
- **Year Founded:** 1993
- **HQ Location:** London, GB
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=17ad62b9d80f8191254aff1e176cfd3e55129b01b5181d9cee82323760187d37&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmerlin_2&secure%5Burl_type%5D=linkedin_company_website)  
429 employees on LinkedIn®

#### Who Uses This Product?

- **Company Size:** 50% Small, 30% Medium

#### What Are Recent G2 Reviews of Merlin?

**["User Friendly Software"](https://www.g2.com/survey_responses/merlin-review-4681854)**

**Rating:** 4.5/5.0 stars

_— Verified User in Education Management_

[Read full review](https://www.g2.com/survey_responses/merlin-review-4681854)

**["Best to use"](https://www.g2.com/survey_responses/merlin-review-1542799)**

**Rating:** 5.0/5.0 stars

_— Verified User in Market Research_

[Read full review](https://www.g2.com/survey_responses/merlin-review-1542799)

#### What Are G2 Users Discussing About Merlin?

- [What is Merlin used for?](https://www.g2.com/discussions/what-is-merlin-used-for)

### [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

#### How Do G2 Users Rate NVIDIA Deep Learning GPU Training System (DIGITS)?

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

#### Who Is the Company Behind NVIDIA Deep Learning GPU Training System (DIGITS)?

- **Seller:** [NVIDIA](https://www.g2.com/sellers/nvidia)
- **Year Founded:** 1993
- **HQ Location:** Santa Clara, CA
- **Twitter:** @nvidia  
2,582,827 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=78ee5403fa8a1bf981ce9ddbc58839ffedf0b07ab7bb703e5734e5f87464a603&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F3608%2F&secure%5Burl_type%5D=linkedin_company_website)  
48,229 employees on LinkedIn®
- **Ownership:** NVDA

#### Who Uses This Product?

- **Top Industries:** Computer Software
- **Company Size:** 52% Small, 35% Medium

#### What Are Recent G2 Reviews of NVIDIA Deep Learning GPU Training System (DIGITS)?

**["The system is super powerful to train AI model"](https://www.g2.com/survey_responses/nvidia-deep-learning-gpu-training-system-digits-review-6909120)**

**Rating:** 5.0/5.0 stars

_— Verified User in Computer Hardware_

[Read full review](https://www.g2.com/survey_responses/nvidia-deep-learning-gpu-training-system-digits-review-6909120)

**["Learning is easy and fast with NVIDIA Deep learning GPU training System"](https://www.g2.com/survey_responses/nvidia-deep-learning-gpu-training-system-digits-review-6507381)**

**Rating:** 5.0/5.0 stars

_— Dr. Jyoti M._

[Read full review](https://www.g2.com/survey_responses/nvidia-deep-learning-gpu-training-system-digits-review-6507381)

#### What Are G2 Users Discussing About NVIDIA Deep Learning GPU Training System (DIGITS)?

- [What is NVIDIA Deep Learning GPU Training System (DIGITS) used for?](https://www.g2.com/discussions/what-is-nvidia-deep-learning-gpu-training-system-digits-used-for)

### [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

#### How Do G2 Users Rate ConvNetJS?

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

#### Who Is the Company Behind ConvNetJS?

- **Seller:** [Stanford NLP Group](https://www.g2.com/sellers/stanford-nlp-group)
- **HQ Location:** Stanford, CA
- **Twitter:** @stanfordnlp  
187,198 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7886df2ed926834e5eb248c77dcfa8e5c815d3ab1f0fe3132ced0dba45868834&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2FNo-Linkedin-Presence-Added-Intentionally-By-DataOps&secure%5Burl_type%5D=linkedin_company_website)  
1 employees on LinkedIn®

#### Who Uses This Product?

- **Company Size:** 38% Large, 38% Small

#### What Are Recent G2 Reviews of ConvNetJS?

**["Best JavaScript Library For ML model Training"](https://www.g2.com/survey_responses/convnetjs-review-8746586)**

**Rating:** 5.0/5.0 stars

_— kunal u._

[Read full review](https://www.g2.com/survey_responses/convnetjs-review-8746586)

**["ConvNetJS: Deep learning through your browsers!"](https://www.g2.com/survey_responses/convnetjs-review-9051891)**

**Rating:** 4.0/5.0 stars

_— Shiv S._

[Read full review](https://www.g2.com/survey_responses/convnetjs-review-9051891)

### [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

#### How Do G2 Users Rate node-fann?

- **Ease of Use:** 8.5/10 (Category avg: 8.1/10)
- **Quality of Support:** 9.0/10 (Category avg: 8.0/10)

#### Who Is the Company Behind node-fann?

- **Seller:** [node-fann](https://www.g2.com/sellers/node-fann)
- **HQ Location:** N/A
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7886df2ed926834e5eb248c77dcfa8e5c815d3ab1f0fe3132ced0dba45868834&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2FNo-Linkedin-Presence-Added-Intentionally-By-DataOps&secure%5Burl_type%5D=linkedin_company_website)  
1 employees on LinkedIn®

#### Who Uses This Product?

- **Company Size:** 50% Medium, 42% Small

#### What Are Recent G2 Reviews of node-fann?

**["Best Valuable Open-Source Artificial Intelligence Library"](https://www.g2.com/survey_responses/node-fann-review-8986880)**

**Rating:** 4.5/5.0 stars

_— vijay b._

[Read full review](https://www.g2.com/survey_responses/node-fann-review-8986880)

**["Empowering Neural Network Development: Unleashing the Potential of the FANN"](https://www.g2.com/survey_responses/node-fann-review-8920756)**

**Rating:** 4.5/5.0 stars

_— Ritik S._

[Read full review](https://www.g2.com/survey_responses/node-fann-review-8920756)

#### What Are G2 Users Discussing About node-fann?

- [What is node-fann used for?](https://www.g2.com/discussions/what-is-node-fann-used-for)

### [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

#### How Do G2 Users Rate Neuton AutoML?

- **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 Neuton AutoML?

- **Seller:** [Bell Integrator](https://www.g2.com/sellers/bell-integrator)
- **Year Founded:** 2003
- **HQ Location:** San Jose, CA
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=201e18412b72e0be88371139c75b34c49a50fa64312cd24ba575b16d2ecbb1e7&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fbellintegrator%2F&secure%5Burl_type%5D=linkedin_company_website)  
703 employees on LinkedIn®

#### Who Uses This Product?

- **Company Size:** 35% Small, 35% Large

#### What Are Recent G2 Reviews of Neuton AutoML?

**["Cloud based ML platform for everyone."](https://www.g2.com/survey_responses/neuton-automl-review-8043519)**

**Rating:** 4.5/5.0 stars

_— Abhuday T._

[Read full review](https://www.g2.com/survey_responses/neuton-automl-review-8043519)

**["A Comprehensive and Efficient Solution for Automating Machine Learning Model Development"](https://www.g2.com/survey_responses/neuton-automl-review-7623010)**

**Rating:** 4.5/5.0 stars

_— Rajesh S._

[Read full review](https://www.g2.com/survey_responses/neuton-automl-review-7623010)

#### What Are G2 Users Discussing About Neuton AutoML?

- [What is Neuton AutoML used for?](https://www.g2.com/discussions/what-is-neuton-automl-used-for)

### [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

#### How Do G2 Users Rate SuperLearner?

- **Ease of Use:** 9.3/10 (Category avg: 8.1/10)
- **Quality of Support:** 8.5/10 (Category avg: 8.0/10)

#### Who Is the Company Behind SuperLearner?

- **Seller:** [Super Learner](https://www.g2.com/sellers/super-learner)
- **Year Founded:** 2018
- **HQ Location:** Miami, US
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=8e925e105d7ed8e49ba89df17a2dd46fc49fef34176ca2804915975c91de5df3&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmeet-super&secure%5Burl_type%5D=linkedin_company_website)  
1,248 employees on LinkedIn®

#### Who Uses This Product?

- **Company Size:** 38% Small, 31% Large

#### What Are Recent G2 Reviews of SuperLearner?

**["Review SuperLerner Software"](https://www.g2.com/survey_responses/superlearner-review-5416354)**

**Rating:** 4.0/5.0 stars

_— Govind K._

[Read full review](https://www.g2.com/survey_responses/superlearner-review-5416354)

**["Remarkable Online Study Tool"](https://www.g2.com/survey_responses/superlearner-review-5460984)**

**Rating:** 5.0/5.0 stars

_— Raghuraman S._

[Read full review](https://www.g2.com/survey_responses/superlearner-review-5460984)

#### What Are G2 Users Discussing About SuperLearner?

- [What is SuperLearner used for?](https://www.g2.com/discussions/what-is-superlearner-used-for)

### [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

#### How Do G2 Users Rate gobrain?

- **Ease of Use:** 8.6/10 (Category avg: 8.1/10)
- **Quality of Support:** 8.9/10 (Category avg: 8.0/10)

#### Who Is the Company Behind gobrain?

- **Seller:** [gobrain](https://www.g2.com/sellers/gobrain)
- **HQ Location:** N/A
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7886df2ed926834e5eb248c77dcfa8e5c815d3ab1f0fe3132ced0dba45868834&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2FNo-Linkedin-Presence-Added-Intentionally-By-DataOps&secure%5Burl_type%5D=linkedin_company_website)  
1 employees on LinkedIn®

#### Who Uses This Product?

- **Company Size:** 64% Small, 36% Medium

#### What Are Recent G2 Reviews of gobrain?

**["GoBrain: Empowering Businesses with Accurate and Customizable Data Analysis"](https://www.g2.com/survey_responses/gobrain-review-8274914)**

**Rating:** 5.0/5.0 stars

_— Walid R._

[Read full review](https://www.g2.com/survey_responses/gobrain-review-8274914)

**["Fascinating Approach to Using Go as AI"](https://www.g2.com/survey_responses/gobrain-review-8734774)**

**Rating:** 5.0/5.0 stars

_— Emily S._

[Read full review](https://www.g2.com/survey_responses/gobrain-review-8734774)

#### What Are G2 Users Discussing About gobrain?

- [What is gobrain used for?](https://www.g2.com/discussions/what-is-gobrain-used-for) - 1 upvote

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 ![Tian Lin](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Tian Lin")
TL

Researched and written by [Tian Lin](https://research.g2.com/insights/author/tian-lin)

Updated April 9, 2026

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.

Show More

* * *

## 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.