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Keras VS QuickBase

Compare Keras VS QuickBase and see what are their differences

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Keras logo Keras

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

QuickBase logo QuickBase

Quickbase provides a no-code operational agility platform that enables organizations to improve operations through real time insights and automation across complex processes and disparate systems. ​​
  • Keras Landing page
    Landing page //
    2023-10-16
  • QuickBase Landing page
    Landing page //
    2023-08-27

Quickbase provides a no-code operational agility platform that enables organizations to improve operations through real-time insights and automation across complex processes and disparate systems. Our goal is to help companies achieve operational agility—to be more responsive to customers, more engaging to employees and as adaptable as possible to what’s next. Quickbase helps nearly 6,000 customers, including over 80 percent of the Fortune 50. Visit www.quickbase.com to learn more.

Keras features and specs

  • 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 of Keras

  • 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.

QuickBase features and specs

  • Customizability
    QuickBase offers extensive customization options, allowing users to tailor databases and applications to fit specific business needs without requiring deep technical expertise.
  • User-friendly Interface
    The platform features an intuitive interface which makes it easy for users with minimal technical background to navigate and manage data.
  • Integration Capabilities
    QuickBase provides robust integration options with other software and services through APIs, ensuring seamless workflow automation and data synchronization.
  • Rapid Development
    Businesses can quickly develop and deploy new applications, significantly reducing time-to-market for new solutions.
  • Strong Security
    QuickBase employs strong security measures including data encryption, compliance certifications, and user access controls to ensure data safety.
  • Scalability
    The platform is highly scalable, capable of handling growth in data volume and user base without performance degradation.

Possible disadvantages of QuickBase

  • Cost
    QuickBase can be expensive compared to other similar platforms, particularly for small businesses or startups with limited budgets.
  • Learning Curve for Advanced Features
    While basic operations are user-friendly, more advanced features and customization may require a steep learning curve.
  • Limited Native Mobile Support
    The native mobile experience is somewhat limited, which may impact users who require robust mobile functionalities.
  • Dependency on Internet
    As a cloud-based platform, QuickBase requires a steady internet connection for optimal performance, which might be a limitation in areas with poor connectivity.
  • Limited Advanced Reporting
    While QuickBase offers basic reporting tools, users may find the advanced reporting capabilities to be lacking compared to dedicated BI tools.
  • Complex Pricing Structure
    The pricing tiers and add-on costs can be complex to navigate, making it challenging for businesses to predict total expenses accurately.

Keras videos

+ Add

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

More videos:

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

QuickBase videos

+ Add

Part 1: Quickbase Basics

More videos:

  • Review - Work at the Speed of Now with Quickbase

Category Popularity

0-100% (relative to Keras and QuickBase)

Keras

QuickBase

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Project Management
0 0%
Project Management
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Task Management
0 0%
Task Management
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Keras and QuickBase

Keras Reviews

10 Python Libraries for Computer Vision
TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.
Source: clouddevs.com
25 Python Frameworks to Master
Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by François Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.
Source: kinsta.com
15 data science tools to consider using in 2021
Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It's an open source deep learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends but was tied exclusively to TensorFlow starting with...
Source: searchbusinessanalytics.techtarget.com

QuickBase Reviews

12 Best JIRA Alternatives in 2019
QuickBase is one of the friendly and highly useful JIRA alternatives which can be used instead of JIRA. The platform is highly flexible, and it can adapt to any work environment. This tool can be a good comparison as JIRA vs QuickBase.
Source: www.guru99.com

Social recommendations and mentions

Based on our record, Keras seems to be more popular. It has been mentiond 35 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Keras mentions (35)

  • Top Programming Languages for AI Development in 2025
    The unchallenged leader in AI development is still Python. And Keras, and robust community support. - Source: dev.to / 18 days ago
  • Top 8 OpenSource Tools for AI Startups
    If you need simplicity, Keras is a great high-level API built on top of TensorFlow. It lets you quickly prototype neural networks without worrying about low-level implementations. Keras is perfect for getting those first models up and running—an essential part of the startup hustle. - Source: dev.to / 7 months ago
  • Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
    At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck. - Source: dev.to / 8 months ago
  • Using Google Magika to build an AI-powered file type detector
    The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models. - Source: dev.to / 11 months ago
  • My Favorite DevTools to Build AI/ML Applications!
    As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / about 1 year ago
View more

QuickBase mentions (0)

We have not tracked any mentions of QuickBase yet. Tracking of QuickBase recommendations started around Mar 2021.

What are some alternatives?

When comparing Keras and QuickBase, you can also consider the following products

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

Asana - Asana project management is an effort to re-imagine how we work together, through modern productivity software. Fast and versatile, Asana helps individuals and groups get more done.

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Basecamp - A simple and elegant project management system.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Teamgantt - Project Management Software Company

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