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Keras VS SpreadsheetWEB

Compare Keras VS SpreadsheetWEB and see what are their differences

Keras

Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

Rating
0 reviews
Pricing
Open source
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Alternatives Is it good?
SpreadsheetWEB

SpreadsheetWEB is a no-code platform that helps businesses translate their collective Microsoft Excel expertise into robust web applications with limited or no reliance on IT resources.

Rating
0 reviews
Pricing
Freemium Free trial $30 / Monthly
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Alternatives Is it good?
KeptPDF
Redact, edit, OCR, and sign PDFs entirely in your browser. The file never leaves your device. Free, no account needed. sponsored
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Which is more popular?

Based on our record, Keras seems to be more popular. It has been mentioned 35 times since March 2021.

social mentions
35 vs 0
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
134 vs 46

Base details

Website, pricing, platforms and company facts side by side.

Keras
SpreadsheetWEB
Website keras.io spreadsheetweb.com
Pricing
Open source
Freemium Free trial $30 / Monthly Official pricing
Company — Startup from the United States · 2006
Listed in
Data Science And Machine Learning Data Science Tools OCR Python Tools +1
Spreadsheets Spreadsheets As A Backend Spreadsheet Converter Web App Rapid Application Development No Code Low Code +4

About Keras and SpreadsheetWEB

In their own words, as submitted to SaaSHub.

Keras
SpreadsheetWEB

No description of Keras yet.

Pagos helps businesses translate their collective Microsoft Excel expertise into robust web applications with limited reliance on IT resources. The SpreadsheetWEB platform empowers ordinary Excel users to create secure, responsive, database-driven applications directly from their Excel...

Read more about SpreadsheetWEB

Features and specs

What each product offers, as listed by its team.

Keras 6 features
SpreadsheetWEB 6 features
  • User-Friendly
    Keras provides a simple and intuitive interface, making it easy for beginners to start building and training models without needing extensive experience in deep learning.
  • Modularity
    Keras follows a modular design, allowing users to easily plug in different neural network components, such as layers, activation functions, and optimizers, to create complex models.
  • Pre-trained Models
    Keras includes a wide range of pre-trained models and offers easy integration with transfer learning techniques, reducing the time required to achieve good results on new tasks.
  • Integration with TensorFlow
    As part of TensorFlow’s ecosystem, Keras provides deep integration with TensorFlow functionalities, enabling users to leverage TensorFlow's powerful features and performance optimizations.
  • Extensive Documentation
    Keras has comprehensive and well-organized documentation, along with numerous tutorials and code examples, making it easier for developers to learn and use the framework.
  • Community Support
    Keras benefits from a large and active community, which provides support through forums, GitHub, and specialized user groups, facilitating the resolution of issues and sharing of best practices.

Possible disadvantages

  • Performance Limitations
    Due to its high-level abstraction, Keras may incur performance overheads, making it less suitable for scenarios requiring extremely fast execution and low-level optimizations.
  • Limited Low-Level Control
    The simplicity and abstraction of Keras can be a downside for advanced users who need fine-grained control over model components and custom operations, which may require them to resort to lower-level frameworks.
  • Scalability Issues
    In some complex applications and large-scale deployments, Keras might face scalability challenges, where more specialized or low-level frameworks could handle such tasks more efficiently.
  • Dependency on TensorFlow
    While the integration with TensorFlow is generally an advantage, it also means that the performance and features of Keras are closely tied to the development and updates of TensorFlow.
  • Lagging Behind Latest Research
    Keras, being a user-friendly high-level API, might not always incorporate the latest cutting-edge research advancements in deep learning as quickly as more research-oriented frameworks.
  • Ease_of_Use
    SpreadsheetWEB offers a user-friendly interface that makes it easy for non-programmers to convert their spreadsheets into web applications without needing extensive coding knowledge.
  • Excel_Integration
    Seamless integration with Microsoft Excel allows users to leverage their existing Excel models, formulas, and data without needing to rebuild them from scratch.
  • Customization
    The platform provides advanced customization options, allowing users to design their web applications to match specific business needs and workflows.
  • Data_Connectivity
    Supports various data sources, including databases and cloud services, enabling real-time data updates and greater flexibility in data management.
  • Collaboration_Features
    Offers collaborative features like user access control and version management, making it easier for teams to work together on the same application.
  • Mobile_Compatibility
    Applications built with SpreadsheetWEB are fully responsive and can be accessed from mobile devices, providing flexibility and mobility for users.

Analysis

An editorial look at what each product does well and who it suits.

Keras
SpreadsheetWEB

Overall verdict

  • Keras is a solid choice for deep learning projects, offering simplicity and flexibility without sacrificing performance. It is well-suited for educational purposes, research, and even deploying models in production environments.

Why this product is good

  • Keras is widely regarded as a good deep learning library because it provides a user-friendly API that allows for easy and fast prototyping of neural networks. It is built on top of other libraries like TensorFlow, making it robust and efficient for both beginners and experienced developers. Its modularity, extensibility, and compatibility with other tools and libraries make it a popular choice for developing deep learning models.

Recommended for

  • Beginners who are new to deep learning
  • Researchers looking for an easy-to-use platform for prototyping models
  • Developers working on projects that require quick experimentation and development
  • Individuals and companies deploying models into production environments

No analysis of SpreadsheetWEB yet.

Videos

Walkthroughs and reviews on video.

Keras 3 videos + Add
SpreadsheetWEB 1 video + Add

3. Deep Learning Tutorial (Tensorflow2.0, Keras & Python) - Movie Review Classification

More videos

  • - Movie Review Classifier in Keras | Deep Learning | Binary Classifier
  • - EKOR KERAS!! Review and Bike Check DARTMOOR HORNET 2018 // MTB Indonesia

Create an App with SpreadsheetWeb: Timesheets

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Keras
SpreadsheetWEB
100% 100%
Data Science And Machine Learning
0% 0%
0% 0%
Spreadsheets
100% 100%
100% 100%
OCR
0% 0%
0% 0%
Spreadsheets As A Backend
100% 100%

Questions & Answers

As answered by people managing Keras and SpreadsheetWEB.

What makes your product unique?

SpreadsheetWEB's answer:

SpreadsheetWeb enables users to transform complex Excel spreadsheets into secure, interactive web applications without coding, combining ease of use, powerful customization, and robust security.

Why should a person choose your product over its competitors?

SpreadsheetWEB's answer:

SpreadsheetWeb uniquely combines the power and flexibility of Excel with the accessibility and security of web-based applications, enabling users to convert complex spreadsheets into secure, interactive web apps without any coding. Unlike competitors, SpreadsheetWeb supports a comprehensive array of Excel features: including over 500 Excel formulas, conditional logic, LAMBDA, Power Query, and dynamic visualizations, preserving existing spreadsheet logic seamlessly. Additionally, its robust security infrastructure, extensive customization, and powerful API integration capabilities make it the optimal choice for businesses seeking a scalable, enterprise-grade solution to manage spreadsheet proliferation, automate workflows, and centralize data securely, all while maintaining Excel’s ease of use.

How would you describe the primary audience of your product?

SpreadsheetWEB's answer:

SpreadsheetWeb's primary audience consists of business professionals and organizations that rely heavily on complex Excel models and require a secure, scalable, and user-friendly way to transition their spreadsheets into web-based applications without coding. This includes financial analysts, actuaries, product managers, sales teams, risk management professionals, and compliance officers who regularly handle intricate formulas, data calculations, or regulatory processes. Additionally, SpreadsheetWeb appeals strongly to enterprises seeking centralized data management, security, and integration capabilities to streamline workflows, reduce spreadsheet proliferation, enhance collaboration, and enforce regulatory compliance efficiently across their organizations.

What's the story behind your product?

SpreadsheetWEB's answer:

SpreadsheetWeb, developed by Pagos, Inc. in 2006, was created to help businesses convert Excel-based models into secure, scalable web applications without the need for coding. Originating from the financial and insurance sectors, where Excel is heavily relied upon, it addressed challenges like version control, lack of access management, and IT bottlenecks. Unlike other no-code platforms, SpreadsheetWeb preserves the original spreadsheet logic, enabling business users to build web applications directly from Excel files while incorporating features like role-based access, workflow automation, and API integration. Over time, it has evolved to support both cloud and on-premise deployments and is now used across industries such as healthcare, insurance, manufacturing, and consulting to digitize processes, enhance security, and reduce reliance on custom software development.

Which are the primary technologies used for building your product?

SpreadsheetWEB's answer:

SpreadsheetWeb is primarily built using Microsoft .NET technologies, with ASP.NET serving as the backbone for web application development and C# as the core programming language. It leverages Microsoft Excel as the logic engine, allowing native spreadsheet formulas and functions to drive web-based applications. The platform uses SQL Server for data storage and management, and integrates with other systems via RESTful APIs. On the front end, it employs standard web technologies like HTML, CSS, and JavaScript, and supports custom UI design through a drag-and-drop interface. For deployments, it offers options across cloud, private cloud, and on-premise environments, ensuring flexibility and security for various enterprise needs.

User comments

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Reviews and articles

External articles and on-site reviews we used to compare the two products.

Keras no reviews yet
SpreadsheetWEB no reviews yet

We have no reviews of SpreadsheetWEB yet. Be the first one to post

Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Keras 35 mentions
SpreadsheetWEB 0 mentions

View more

Tracking SpreadsheetWEB since Mar 2021.

Alternatives to Keras and SpreadsheetWEB

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