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Keras VS Backendless

Compare Keras VS Backendless 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.

Backendless logo Backendless

Backendless is a mobile Backend as a Service (mBaaS) platform.
  • Keras Landing page
    Landing page //
    2023-10-16
  • Backendless Landing page
    Landing page //
    2023-07-20

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.

Backendless features and specs

  • Codeless Development
    Backendless offers a 'Codeless' feature, which allows users to build backend logic without writing any code. This is particularly beneficial for those who are not familiar with complex coding languages.
  • Real-Time Database
    The platform provides real-time data synchronization, allowing applications to update data instantly across all clients. This is essential for interactive applications such as chat apps and real-time data feeds.
  • API Services
    Backendless allows the creation of REST and SOAP APIs effortlessly. This makes it easier to integrate with other services and provides a clear pathway for extending app functionality.
  • User Management
    The platform comes with built-in user management features such as registration, login, password recovery, and social logins. This helps in reducing the effort required to implement user authentication and authorization.
  • Mobile and Web App Support
    Backendless supports both mobile (iOS/Android) and web applications, offering SDKs for multiple platforms which streamlines the development process.

Possible disadvantages of Backendless

  • Pricing
    Although Backendless offers a free tier, many features and higher usage levels are locked behind a paywall. This may be prohibitive for startups or small projects with limited budgets.
  • Learning Curve
    Even though Backendless offers codeless development, mastering the platform as a whole can be challenging for beginners. There are many features and settings that require some time to understand fully.
  • Vendor Lock-In
    Relying too much on Backendless-specific features can create difficulties if you decide to migrate to another backend service in the future. The migration process can be complex and time-consuming.
  • Limited Customization
    While Backendless offers many out-of-the-box features, there can be limitations in terms of customizing the backend behavior in comparison to building a custom backend.
  • Community and Support
    The community around Backendless is smaller compared to more established backend solutions like Firebase. This can make finding community support, third-party plugins, or comprehensive tutorials harder.

Analysis of Keras

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

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

Backendless videos

+ Add

Backendless 5 Release Overview (webinar)

More videos:

  • Review - Functionality Visibility Control in Backendless Console
  • Review - Backendless version 3.0 Overview

Category Popularity

0-100% (relative to Keras and Backendless)

Keras

Backendless

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Developer Tools
0 0%
Developer Tools
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Databases
0 0%
Databases
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 Backendless

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

Backendless Reviews

2023 Firebase Alternatives: Top 10 Open-Source & Free
There are three comprehensive plans of this BaaS vendor: Backendless Cloud, Pro and Managed. But it only opened the pricing details of Backendless Cloud in this regard. Here are the key components of Backendless Cloud pricing:
Source: digitalcruch.com
Firebase Alternatives – Top 10 Competitors
Backendless is a highly scalable mobile Backend-as-a-Service (mBaaS) platform providing gazillion of features, including user authentication, live audio and video streaming, message filtering, push notifications, auto-scalability, data persistence, file storage, geo-location, cloud-code, analytics, and custom business logic. It has it all what you need to build awesome...
Source: blog.back4app.com

Social recommendations and mentions

Based on our record, Keras should be more popular than Backendless. 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 / about 2 months 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 / 8 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 / about 1 year 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

Backendless mentions (21)

  • Ap Developer
    Go here: https://backendless.com/ . If that don't work for you, Let me know and I'll tell you what next to do. Source: about 2 years ago
  • Join the Free Database Training Course From Backendless
    This article first appeared on https://backendless.com. - Source: dev.to / over 2 years ago
  • free-for.dev
    Backendless.com — Mobile and Web Baas, with 1 GB file storage free, push notifications 50000/month, and 1000 data objects in table. - Source: dev.to / over 2 years ago
  • How Much Does Custom Software Development Cost?
    Luckily, instead of building the backend from scratch, some backend Application Programming Interfaces (APIs) are available. Consider the following options: REST API, Firebase, Backendless, and JHipster. Using APIs is a great way to adopt a functional backend with lower custom software development pricing. - Source: dev.to / almost 3 years ago
  • Urgent: Low code / No Code App Builders
    The best no-code/low-code platform for building both the frontend and backend in one place is Backendless. They have the best backend features and a really solid UI Builder that gives you pretty much all capabilities you'll likely need. Source: almost 3 years ago
View more

What are some alternatives?

When comparing Keras and Backendless, 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.

Datomic - The fully transactional, cloud-ready, distributed database

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

MarkLogic Server - MarkLogic Server is a multi-model database that has both NoSQL and trusted enterprise data management capabilities.

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

Valentina Server - Valentina Server is 3 in 1: Valentina DB Server / SQLite Server / Report Server

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