# 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=146295&focus%5B%5D=21666&focus%5B%5D=21613)

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

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=pytorch&focus%5B%5D=keras&focus%5B%5D=knet)

### [Google Cloud Deep Learning Containers](https://www.g2.com/es/products/google-cloud-deep-learning-containers/reviews)

Contenedores preconfigurados y optimizados para entornos de aprendizaje profundo.

**Average Rating:** 4.5/5.0

**Total Reviews:** 26

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

- **Facilidad de uso:** 8.4/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 8.2/10 (Category avg: 8.1/10)

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

- **Vendedor:** [Google](https://www.g2.com/es/sellers/google)
- **Año de fundación:** 1998
- **Ubicación de la sede:** Mountain View, CA
- **Twitter:** @google  
31,899,995 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®
- **Propiedad:** NASDAQ:GOOG

#### Who Uses This Product?

- **Top Industries:** Tecnología de la información y servicios
- **Company Size:** 35% Medium, 32% Large

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

**["Un potente catálogo interno de activos de ML que hizo que compartir cuadernos y flujos de trabajo fuera sencillo"](https://www.g2.com/es/survey_responses/google-cloud-deep-learning-containers-review-13166573)**

**Rating:** 4.0/5.0 stars

_— Luca P._

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

**["Ahorra tiempo de configuración con PyTorch preinstalado y colaboración fluida en equipo"](https://www.g2.com/es/survey_responses/google-cloud-deep-learning-containers-review-13175554)**

**Rating:** 5.0/5.0 stars

_— Javier C._

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

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

- [¿Para qué se utilizan los contenedores de aprendizaje profundo de Google Cloud?](https://www.g2.com/es/discussions/what-is-google-cloud-deep-learning-containers-used-for) - 1 upvote

### [AWS Deep Learning AMIs](https://www.g2.com/es/products/aws-deep-learning-amis/reviews)

Las AMIs de Deep Learning de AWS están diseñadas para equipar a los científicos de datos, practicantes de aprendizaje automático y científicos de investigación con la infraestructura y las herramientas para acelerar el trabajo en aprendizaje profundo, en la nube, a cualquier escala.

**Average Rating:** 4.4/5.0

**Total Reviews:** 24

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

- **Facilidad de uso:** 9.1/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 8.8/10 (Category avg: 8.1/10)

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

- **Vendedor:** [Amazon Web Services (AWS)](https://www.g2.com/es/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Año de fundación:** 2006
- **Ubicación de la sede:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®
- **Propiedad:** NASDAQ: AMZN

#### Who Uses This Product?

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

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

**["AWS Deep Learning AMIs: Configuración de IA/ML rápida y preconfigurada que aumenta la productividad"](https://www.g2.com/es/survey_responses/aws-deep-learning-amis-review-12838360)**

**Rating:** 5.0/5.0 stars

_— Govind J._

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

**["Entorno de IA preconfigurado y optimizado con AWS Deep Learning AMI"](https://www.g2.com/es/survey_responses/aws-deep-learning-amis-review-12883890)**

**Rating:** 4.5/5.0 stars

_— Prabhat C._

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

### [Google Cloud Deep Learning VM Image](https://www.g2.com/es/products/google-cloud-deep-learning-vm-image/reviews)

Imagen de VM de Aprendizaje Profundo VMs preconfiguradas para aplicaciones de aprendizaje profundo.

**Average Rating:** 4.3/5.0

**Total Reviews:** 18

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

- **Facilidad de uso:** 8.4/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 7.9/10 (Category avg: 8.1/10)

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

- **Vendedor:** [Google](https://www.g2.com/es/sellers/google)
- **Año de fundación:** 1998
- **Ubicación de la sede:** Mountain View, CA
- **Twitter:** @google  
31,899,995 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®
- **Propiedad:** NASDAQ:GOOG

#### Who Uses This Product?

- **Company Size:** 53% Small, 37% Medium

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

**["Victoria del Aprendizaje Profundo"](https://www.g2.com/es/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/es/survey_responses/google-cloud-deep-learning-vm-image-review-13180464)

**["Configuración fácil con marcos de IA preinstalados y un fuerte rendimiento de GPU"](https://www.g2.com/es/survey_responses/google-cloud-deep-learning-vm-image-review-13170907)**

**Rating:** 4.5/5.0 stars

_— LOKESH G._

[Read full review](https://www.g2.com/es/survey_responses/google-cloud-deep-learning-vm-image-review-13170907)

### [AIToolbox](https://www.g2.com/es/products/aitoolbox/reviews)

AIToolbox es un marco integral de Swift diseñado para facilitar el desarrollo e implementación de algoritmos de inteligencia artificial. Ofrece un conjunto de módulos de IA que atienden diversas tareas de aprendizaje automático, convirtiéndolo en un recurso valioso para desarrolladores e investigadores que trabajan dentro del ecosistema de Swift. Características y Funcionalidades Clave: - Grafos y Árboles: Proporciona estructuras de datos y algoritmos para construir y manipular grafos y árboles, esenciales para tareas como procesos de toma de decisiones y representación de datos jerárquicos. - Máquinas de Vectores de Soporte (SVMs): Incluye herramientas para implementar SVMs, permitiendo el análisis de clasificación y regresión al encontrar hiperplanos óptimos en espacios de alta dimensión. - Redes Neuronales: Ofrece componentes para construir y entrenar redes neuronales, facilitando aplicaciones de aprendizaje profundo como el reconocimiento de imágenes y voz. - Análisis de Componentes Principales (PCA): Contiene módulos para la reducción de dimensionalidad a través de PCA, ayudando en la visualización de datos y reducción de ruido. - Agrupamiento K-Means: Proporciona algoritmos para particionar conjuntos de datos en grupos, útil en el reconocimiento de patrones y minería de datos. - Algoritmos Genéticos: Incluye herramientas para problemas de optimización usando algoritmos genéticos, simulando procesos de selección natural para encontrar soluciones óptimas. Valor Principal y Soluciones para el Usuario: AIToolbox aborda la necesidad de una biblioteca nativa de Swift que abarque una amplia gama de funcionalidades de IA. Al integrar múltiples módulos de aprendizaje automático en un solo marco, simplifica el proceso de desarrollo para los desarrolladores de Swift, eliminando la necesidad de depender de bibliotecas o lenguajes externos. Esta consolidación mejora la eficiencia, promueve la consistencia del código y acelera el despliegue de aplicaciones impulsadas por IA en plataformas de Apple.

**Average Rating:** 4.4/5.0

**Total Reviews:** 35

#### How Do G2 Users Rate AIToolbox?

- **Facilidad de uso:** 8.8/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 8.9/10 (Category avg: 8.1/10)

#### Who Is the Company Behind AIToolbox?

- **Vendedor:** [AIToolbox](https://www.g2.com/es/sellers/aitoolbox)
- **Ubicación de la sede:** N/A
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Tecnología de la información y servicios, Software de Computadora
- **Company Size:** 54% Small, 40% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios aprecian la **facilidad de uso** de AIToolbox, encontrándolo conveniente para acceder a varias herramientas de IA en un solo lugar.
- Los usuarios aprecian la **variedad de modelos** de AIToolbox, lo que facilita encontrar y usar herramientas de IA esenciales.
- Los usuarios valoran la **cobertura integral de IA** de AIToolbox, mejorando la conveniencia y eficiencia en los proyectos de desarrollo.
- Los usuarios aprecian las **fáciles integraciones** con varios módulos de IA, mejorando la velocidad de desarrollo y la conveniencia en la implementación.
- Los usuarios aprecian la **amplia gama de herramientas de IA** en AIToolbox, lo que hace que la experimentación y la aplicación sean fáciles y eficientes.

##### Cons

- Los usuarios luchan con la **inexactitud** de AIToolbox, enfrentando problemas como banderas inexplicadas y lógica alucinatoria durante los análisis.
- Los usuarios notan las **características limitadas** de AIToolbox, a menudo encontrándolas básicas y carentes de opciones avanzadas de personalización.
- Los usuarios están frustrados con la **falta de transparencia y los errores frecuentes** en la funcionalidad de IA de AIToolbox durante las transacciones.
- Los usuarios informan de **problemas de compatibilidad** con la versión MINOR, causando interrupciones en la producción y problemas con las operaciones de base de datos de alta frecuencia.
- Los usuarios encuentran el **configuración compleja** de AIToolbox desafiante, sugiriendo mejoras para una integración más fácil y eficiencia en el flujo de trabajo.

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

**["Generoso nivel gratuito y fácil creación de publicaciones sociales impulsadas por IA"](https://www.g2.com/es/survey_responses/aitoolbox-review-12213473)**

**Rating:** 5.0/5.0 stars

_— Tony P._

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

**["Integración flexible y automatización poderosa en una sola plataforma"](https://www.g2.com/es/survey_responses/aitoolbox-review-12473904)**

**Rating:** 4.5/5.0 stars

_— Alexis V._

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

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

- [¿Para qué se utiliza AIToolbox?](https://www.g2.com/es/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/es/products/microsoft-cognitive-toolkit-formerly-cntk/reviews)

Microsoft Cognitive Toolkit es un conjunto de herramientas de código abierto y de calidad comercial que permite al usuario aprovechar la inteligencia dentro de conjuntos de datos masivos a través del aprendizaje profundo al proporcionar escalabilidad, velocidad y precisión sin compromisos con calidad de grado comercial y compatibilidad con los lenguajes de programación y algoritmos que ya utiliza.

**Average Rating:** 4.2/5.0

**Total Reviews:** 22

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

- **Facilidad de uso:** 8.0/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 8.1/10 (Category avg: 8.1/10)

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

- **Vendedor:** [Microsoft](https://www.g2.com/es/sellers/microsoft)
- **Año de fundación:** 1975
- **Ubicación de la sede:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®
- **Propiedad:** MSFT

#### Who Uses This Product?

- **Top Industries:** Software de Computadora, Tecnología de la información y servicios
- **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

- Los usuarios valoran el **flujo de trabajo eficiente** de Microsoft Cognitive Toolkit, mejorando su productividad y gestión de proyectos.

##### Cons

- Los usuarios encuentran el Microsoft Cognitive Toolkit **abrumador debido a su complejidad** , lo que puede obstaculizar su uso efectivo.
- Los usuarios encuentran la **curva de aprendizaje abrumadora** , lo que hace que sea un desafío usar eficazmente Microsoft Cognitive Toolkit.

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

**["Era un buen producto. Fácil de usar y el soporte fue útil."](https://www.g2.com/es/survey_responses/microsoft-cognitive-toolkit-formerly-cntk-review-10277326)**

**Rating:** 4.0/5.0 stars

_— m p._

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

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

**Rating:** 4.5/5.0 stars

_— Rajneesh K._

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

### [PyTorch](https://www.g2.com/es/products/pytorch/reviews)

PyTorch es un marco de aprendizaje automático de código abierto que acelera la transición del prototipado de investigación al despliegue en producción. Desarrollado por Meta AI y ahora gobernado por la Fundación PyTorch bajo la Fundación Linux, PyTorch es ampliamente utilizado para aplicaciones en visión por computadora, procesamiento de lenguaje natural y más. Su gráfico de computación dinámico y su interfaz intuitiva en Python lo convierten en una opción preferida para investigadores y desarrolladores que buscan construir y desplegar modelos de aprendizaje profundo de manera eficiente. Características y Funcionalidades Clave: - Gráfico de Computación Dinámico: Permite una construcción de modelos flexible y eficiente, habilitando cambios en la arquitectura de la red durante el tiempo de ejecución. - Tensores y Autograd: Utiliza tensores como estructuras de datos fundamentales, similares a los arrays de NumPy, con soporte para diferenciación automática para agilizar el cálculo de gradientes. - API de Redes Neuronales: Proporciona un marco modular para construir redes neuronales con capas predefinidas, funciones de activación y funciones de pérdida, facilitando la creación de modelos complejos. - Entrenamiento Distribuido: Ofrece soporte nativo para entrenamiento distribuido, optimizando el rendimiento a través de múltiples GPUs y nodos, lo cual es esencial para escalar modelos grandes. - TorchScript: Permite la transición de la ejecución ansiosa a la ejecución en gráfico, permitiendo que los modelos sean serializados y optimizados para su despliegue en entornos de producción. - TorchServe: Una herramienta para desplegar modelos de PyTorch a escala, soportando características como el servicio de múltiples modelos, registro, métricas y endpoints RESTful para la integración de aplicaciones. - Soporte Móvil (Experimental): Extiende las capacidades de PyTorch a plataformas móviles, permitiendo que los modelos se desplieguen en dispositivos iOS y Android. - Ecosistema Robusto: Apoyado por una comunidad activa, PyTorch ofrece un rico ecosistema de herramientas y bibliotecas para varios dominios, incluyendo visión por computadora y aprendizaje por refuerzo. - Soporte ONNX: Facilita la exportación de modelos en el formato Open Neural Network Exchange (ONNX) para compatibilidad con otras plataformas y entornos de ejecución. Valor Principal y Soluciones para Usuarios: El valor principal de PyTorch radica en su capacidad para proporcionar un camino sin fisuras desde la investigación hasta la producción. Su gráfico de computación dinámico y su interfaz amigable para el usuario permiten un prototipado y experimentación rápidos, permitiendo a los investigadores iterar rápidamente en los diseños de modelos. Para los desarrolladores, el soporte de PyTorch para el entrenamiento distribuido y herramientas como TorchServe simplifican el despliegue de modelos a escala, reduciendo el tiempo y la complejidad asociados con llevar modelos de aprendizaje automático a producción. Además, el extenso ecosistema y el soporte comunitario aseguran que los usuarios tengan acceso a una amplia gama de recursos y herramientas para abordar diversos desafíos de aprendizaje automático.

**Average Rating:** 4.5/5.0

**Total Reviews:** 22

#### How Do G2 Users Rate PyTorch?

- **Facilidad de uso:** 8.7/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 8.1/10 (Category avg: 8.1/10)

#### Who Is the Company Behind PyTorch?

- **Vendedor:** [Jetware](https://www.g2.com/es/sellers/jetware-c6839872-6292-4a7b-973d-ac6da2ceaa45)
- **Año de fundación:** 2017
- **Ubicación de la sede:** Roma, IT
- **Twitter:** @jetware\_io  
25 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios aprecian la **naturaleza intuitiva** de PyTorch, disfrutando de la fácil experimentación y depuración para sus proyectos.
- Los usuarios valoran la **extensa documentación** de PyTorch, que mejora significativamente su experiencia de aprendizaje y desarrollo.
- Los usuarios encuentran que la **interfaz intuitiva** de PyTorch mejora su experiencia, facilitando la experimentación y depuración para los desarrolladores.
- A los usuarios les encanta el **diseño intuitivo** de PyTorch, lo que hace que la experimentación y la depuración sean sencillas y eficientes.
- Los usuarios valoran las **capacidades intuitivas de resolución de problemas** de PyTorch, mejorando la experimentación y la depuración para los desarrolladores.

##### Cons

- Los usuarios encuentran que la **complejidad** de desplegar modelos en PyTorch puede obstaculizar los flujos de trabajo de producción fluidos, lo que requiere herramientas adicionales.
- Los usuarios encuentran que las **curvas de aprendizaje** difíciles para las funciones avanzadas en PyTorch obstaculizan un despliegue y escalado más fluido del modelo.
- Los usuarios encuentran **difícil la navegación** en PyTorch debido a la necesidad de una configuración extensa y avanzada para el despliegue.

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

**["Poderosa biblioteca de Python de código abierto para aprendizaje automático"](https://www.g2.com/es/survey_responses/pytorch-review-13093960)**

**Rating:** 4.5/5.0 stars

_— Usuario verificado en Tecnología de la información y servicios_

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

**["Marco de Aprendizaje Profundo Flexible e Intuitivo"](https://www.g2.com/es/survey_responses/pytorch-review-11698021)**

**Rating:** 4.5/5.0 stars

_— Jagdish P._

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

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

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

### [Keras](https://www.g2.com/es/products/keras/reviews)

Keras es una biblioteca de redes neuronales, escrita en Python y capaz de ejecutarse sobre TensorFlow o Theano.

**Average Rating:** 4.6/5.0

**Total Reviews:** 64

#### How Do G2 Users Rate Keras?

- **Facilidad de uso:** 8.9/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 7.8/10 (Category avg: 8.1/10)

#### Who Is the Company Behind Keras?

- **Vendedor:** [Keras](https://www.g2.com/es/sellers/keras)
- **Año de fundación:** 2016
- **Ubicación de la sede:** N/A
- **Twitter:** @keras  
26 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Científico de Datos
- **Top Industries:** Software de Computadora, Tecnología de la información y servicios
- **Company Size:** 38% Small, 32% Medium

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

**["Mejor marco de DL"](https://www.g2.com/es/survey_responses/keras-review-8604851)**

**Rating:** 5.0/5.0 stars

_— Aakash Kumar A._

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

**["Una herramienta versátil para el aprendizaje automático, la visión por computadora y el aprendizaje profundo"](https://www.g2.com/es/survey_responses/keras-review-12598879)**

**Rating:** 4.0/5.0 stars

_— Usuario verificado en Renovables y Medio Ambiente_

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

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

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

### [Knet](https://www.g2.com/es/products/knet/reviews)

Knet (pronunciado "kay-net") es un marco de aprendizaje profundo implementado en Julia que permite la definición y el entrenamiento de modelos de aprendizaje automático utilizando todo el poder y la expresividad de Julia.

**Average Rating:** 4.3/5.0

**Total Reviews:** 12

#### How Do G2 Users Rate Knet?

- **Facilidad de uso:** 8.9/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 9.0/10 (Category avg: 8.1/10)

#### Who Is the Company Behind Knet?

- **Vendedor:** [Knet](https://www.g2.com/es/sellers/knet)
- **Año de fundación:** 1990
- **Ubicación de la sede:** Kuwait, Kuwait
- **Twitter:** @knet  
68 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®

#### Who Uses This Product?

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

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

**["Knet el grande"](https://www.g2.com/es/survey_responses/knet-review-7062700)**

**Rating:** 5.0/5.0 stars

_— Usuario verificado en Software de Computadora_

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

**["El mejor lugar para aprender más sobre todo."](https://www.g2.com/es/survey_responses/knet-review-6811665)**

**Rating:** 5.0/5.0 stars

_— Eesha J._

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

### [NVIDIA Deep Learning GPU Training System (DIGITS)](https://www.g2.com/es/products/nvidia-deep-learning-gpu-training-system-digits/reviews)

NVIDIA Deep Learning GPU Training System (DIGITS) aprendizaje profundo para ciencia de datos e investigación para diseñar rápidamente redes neuronales profundas (DNN) para tareas de clasificación de imágenes y detección de objetos utilizando visualización del comportamiento de la red en tiempo real.

**Average Rating:** 4.5/5.0

**Total Reviews:** 22

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

- **Facilidad de uso:** 8.3/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 7.8/10 (Category avg: 8.1/10)

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

- **Vendedor:** [NVIDIA](https://www.g2.com/es/sellers/nvidia)
- **Año de fundación:** 1993
- **Ubicación de la sede:** Santa Clara, CA
- **Twitter:** @nvidia  
2,582,827 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®
- **Propiedad:** NVDA

#### Who Uses This Product?

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

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

**["El sistema es muy potente para entrenar modelos de IA."](https://www.g2.com/es/survey_responses/nvidia-deep-learning-gpu-training-system-digits-review-6909120)**

**Rating:** 5.0/5.0 stars

_— Usuario verificado en Hardware de Computadora_

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

**["El aprendizaje es fácil y rápido con el sistema de entrenamiento de GPU de aprendizaje profundo de NVIDIA."](https://www.g2.com/es/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/es/survey_responses/nvidia-deep-learning-gpu-training-system-digits-review-6507381)

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

- [¿Para qué se utiliza el Sistema de Entrenamiento de GPU de Aprendizaje Profundo de NVIDIA (DIGITS)?](https://www.g2.com/es/discussions/what-is-nvidia-deep-learning-gpu-training-system-digits-used-for)

### [Merlin](https://www.g2.com/es/products/merlin/reviews)

Merlin es un marco de aprendizaje profundo escrito en Julia, tiene como objetivo proporcionar una biblioteca de aprendizaje profundo rápida, flexible y compacta para el aprendizaje automático.

**Average Rating:** 3.6/5.0

**Total Reviews:** 10

#### How Do G2 Users Rate Merlin?

- **Facilidad de uso:** 8.9/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 6.4/10 (Category avg: 8.1/10)

#### Who Is the Company Behind Merlin?

- **Vendedor:** [Merlin](https://www.g2.com/es/sellers/merlin)
- **Año de fundación:** 1993
- **Ubicación de la sede:** London, GB
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®

#### Who Uses This Product?

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

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

**["Mejor usar"](https://www.g2.com/es/survey_responses/merlin-review-1542799)**

**Rating:** 5.0/5.0 stars

_— Usuario verificado en Investigación de mercado_

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

**["Software fácil de usar"](https://www.g2.com/es/survey_responses/merlin-review-4681854)**

**Rating:** 4.5/5.0 stars

_— Usuario verificado en Gestión Educativa_

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

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

- [¿Para qué se utiliza Merlin?](https://www.g2.com/es/discussions/what-is-merlin-used-for)

### [ConvNetJS](https://www.g2.com/es/products/convnetjs/reviews)

ConvNetJS es una biblioteca de Javascript para entrenar modelos de Aprendizaje Profundo (Redes Neuronales) completamente en un navegador.

**Average Rating:** 3.8/5.0

**Total Reviews:** 13

#### How Do G2 Users Rate ConvNetJS?

- **Facilidad de uso:** 9.3/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 8.0/10 (Category avg: 8.1/10)

#### Who Is the Company Behind ConvNetJS?

- **Vendedor:** [Stanford NLP Group](https://www.g2.com/es/sellers/stanford-nlp-group)
- **Ubicación de la sede:** Stanford, CA
- **Twitter:** @stanfordnlp  
187,198 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®

#### Who Uses This Product?

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

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

**["Mejor biblioteca de JavaScript para el entrenamiento de modelos de ML"](https://www.g2.com/es/survey_responses/convnetjs-review-8746586)**

**Rating:** 5.0/5.0 stars

_— kunal u._

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

**["ConvNetJS: ¡Aprendizaje profundo a través de tus navegadores!"](https://www.g2.com/es/survey_responses/convnetjs-review-9051891)**

**Rating:** 4.0/5.0 stars

_— Shiv S._

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

### [node-fann](https://www.g2.com/es/products/node-fann/reviews)

FANN (Biblioteca de Red Neuronal Artificial Rápida) es una biblioteca de red neuronal de código abierto y gratuita, que implementa redes neuronales artificiales de múltiples capas con soporte tanto para redes completamente conectadas como para redes escasamente conectadas.

**Average Rating:** 4.2/5.0

**Total Reviews:** 12

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

- **Facilidad de uso:** 8.5/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 9.0/10 (Category avg: 8.1/10)

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

- **Vendedor:** [node-fann](https://www.g2.com/es/sellers/node-fann)
- **Ubicación de la sede:** N/A
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®

#### Who Uses This Product?

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

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

**["Mejor biblioteca de inteligencia artificial de código abierto valiosa"](https://www.g2.com/es/survey_responses/node-fann-review-8986880)**

**Rating:** 4.5/5.0 stars

_— vijay b._

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

**["Desarrollo de Redes Neuronales Empoderadas: Liberando el Potencial de la FANN"](https://www.g2.com/es/survey_responses/node-fann-review-8920756)**

**Rating:** 4.5/5.0 stars

_— Ritik S._

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

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

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

### [Neuton AutoML](https://www.g2.com/es/products/neuton-automl/reviews)

Neuton (https://neuton.ai), una nueva solución AutoML, permite a los usuarios construir modelos de IA compactos con solo unos pocos clics y sin necesidad de codificación. Neuton también resulta ser el marco de red neuronal y solución AutoML más EXPLICABLE actualmente disponible en el mercado. Permite a los usuarios evaluar la calidad del modelo desde diversas perspectivas e interpretar los resultados de las predicciones. Oficina de Explicabilidad de Neuton: - Análisis Exploratorio de Datos - Matriz de Importancia de Características con granularidad de clase - Intérprete de Modelo - Matriz de Influencia de Características - Validar Modelo en Nuevos Datos - Indicadores de Relevancia del Modelo a los Datos históricos y para cada predicción - Índice de Calidad del Modelo - Intervalo de Confianza - Lista extensa de métricas compatibles con Diagrama de Radar

**Average Rating:** 4.5/5.0

**Total Reviews:** 17

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

- **Facilidad de uso:** 9.1/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 8.5/10 (Category avg: 8.1/10)

#### Who Is the Company Behind Neuton AutoML?

- **Vendedor:** [Bell Integrator](https://www.g2.com/es/sellers/bell-integrator)
- **Año de fundación:** 2003
- **Ubicación de la sede:** San Jose, CA
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®

#### Who Uses This Product?

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

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

**["Una solución integral y eficiente para automatizar el desarrollo de modelos de aprendizaje automático."](https://www.g2.com/es/survey_responses/neuton-automl-review-7623010)**

**Rating:** 4.5/5.0 stars

_— Rajesh S._

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

**["Plataforma de ML basada en la nube para todos."](https://www.g2.com/es/survey_responses/neuton-automl-review-8043519)**

**Rating:** 4.5/5.0 stars

_— Abhuday T._

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

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

- [¿Para qué se utiliza Neuton AutoML?](https://www.g2.com/es/discussions/what-is-neuton-automl-used-for)

### [SuperLearner](https://www.g2.com/es/products/superlearner/reviews)

SuperLearner es un paquete que implementa el método de predicción super learner y contiene una biblioteca de algoritmos de predicción para ser utilizados en el super learner.

**Average Rating:** 4.5/5.0

**Total Reviews:** 13

#### How Do G2 Users Rate SuperLearner?

- **Facilidad de uso:** 9.3/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 8.5/10 (Category avg: 8.1/10)

#### Who Is the Company Behind SuperLearner?

- **Vendedor:** [Super Learner](https://www.g2.com/es/sellers/super-learner)
- **Año de fundación:** 2018
- **Ubicación de la sede:** Miami, US
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®

#### Who Uses This Product?

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

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

**["Revisión del software SuperLerner"](https://www.g2.com/es/survey_responses/superlearner-review-5416354)**

**Rating:** 4.0/5.0 stars

_— Govind K._

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

**["Herramienta de estudio en línea notable"](https://www.g2.com/es/survey_responses/superlearner-review-5460984)**

**Rating:** 5.0/5.0 stars

_— Raghuraman S._

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

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

- [¿Para qué se utiliza SuperLearner?](https://www.g2.com/es/discussions/what-is-superlearner-used-for)

### [gobrain](https://www.g2.com/es/products/gobrain/reviews)

gobrain es una red neuronal escrita en go que incluye solo funciones básicas de redes neuronales, como Feed Forward y la red neuronal recurrente de Elman.

**Average Rating:** 4.5/5.0

**Total Reviews:** 11

#### How Do G2 Users Rate gobrain?

- **Facilidad de uso:** 8.6/10 (Category avg: 8.1/10)
- **Calidad del soporte:** 8.9/10 (Category avg: 8.1/10)

#### Who Is the Company Behind gobrain?

- **Vendedor:** [gobrain](https://www.g2.com/es/sellers/gobrain)
- **Ubicación de la sede:** N/A
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/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 empleados en LinkedIn®

#### Who Uses This Product?

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

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

**["GoBrain: Empoderando a las empresas con análisis de datos precisos y personalizables."](https://www.g2.com/es/survey_responses/gobrain-review-8274914)**

**Rating:** 5.0/5.0 stars

_— Walid R._

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

**["Enfoque fascinante para usar Go como IA"](https://www.g2.com/es/survey_responses/gobrain-review-8734774)**

**Rating:** 5.0/5.0 stars

_— Emily S._

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

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

- [¿Para qué se utiliza gobrain?](https://www.g2.com/es/discussions/what-is-gobrain-used-for) - 1 upvote

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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.

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## 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.