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Google Open Source VS Keras

Compare Google Open Source VS Keras and see what are their differences

Flagsmith
Flagsmith lets you manage feature flags and remote config across web, mobile and server side applications. Deliver true Continuous Integration. Get builds out faster. Control who has access to new features. We're Open Source. featured
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Contents:
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  3. » Reviews
  4. » Alternatives

Google Open Source logo Google Open Source

All of Googles open source projects under a single umbrella

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.
  • Google Open Source Landing page
    Landing page //
    2023-09-22
  • Keras Landing page
    Landing page //
    2023-10-16

Google Open Source features and specs

  • Community Support
    Google Open Source projects often have large, active communities that contribute to the software's development and provide support.
  • Innovation
    Google frequently publishes cutting-edge projects, allowing developers to utilize the latest in technology and innovation.
  • Quality Documentation
    Google Open Source projects generally come with comprehensive documentation, making it easier for developers to integrate and utilize their tools.
  • Scalability
    Many of Google's open-source projects are designed to scale efficiently, benefiting from Google's extensive experience in handling large-scale systems.
  • Integration with Other Google Services
    Open-source projects from Google often integrate smoothly with other Google services and platforms, providing a cohesive ecosystem.

Possible disadvantages of Google Open Source

  • Dependency on Google
    Being tied to Google ecosystems might lead to dependencies, making it harder for developers to switch to other alternatives.
  • Data Privacy Concerns
    Some developers are wary of data privacy issues when using tools developed by Google, given the company's history with data collection.
  • Complexity
    Google’s projects can sometimes be complex, requiring a steep learning curve for developers who are not familiar with their systems and methodologies.
  • Licensing Issues
    Open-source licensing can sometimes pose challenges, especially for companies trying to ensure compliance with multiple licensing requirements.
  • Longevity and Support
    Not all Google open-source projects have long-term support, and there is a risk that some projects may be abandoned or shelved.

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.

Analysis of Google Open Source

Overall verdict

  • Google Open Source is generally regarded positively within the developer community due to its significant contributions to widely-used projects and its commitment to maintaining open and collaborative development practices.

Why this product is good

  • Google Open Source (opensource.google) is considered good because it hosts a wide array of high-quality projects that are well-maintained and actively supported by Google and the community. These projects often adhere to strong industry standards, providing reliable tools and libraries that developers around the world can use. Additionally, the open-source nature allows developers to contribute, inspect the source code, and modify it to fit their needs, which promotes transparency and innovation.

Recommended for

    This is recommended for developers looking for mature, scalable, and robust open-source solutions. It’s also ideal for organizations seeking to build upon a reliable foundation of tools, tech enthusiasts eager to learn and contribute to open source projects, and anyone interested in the collaborative world of software development.

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

Google Open Source videos

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Keras videos

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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

Category Popularity

0-100% (relative to Google Open Source and Keras)

Google Open Source

Keras

Developer Tools
100 100%
Developer Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Productivity
100 100%
Productivity
0% 0
Data Science Tools
0 0%
Data Science Tools
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 Google Open Source and Keras

Google Open Source Reviews

We have no reviews of Google Open Source yet.
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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

Social recommendations and mentions

Keras might be a bit more popular than Google Open Source. We know about 35 links to it since March 2021 and only 25 links to Google Open Source. 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.

Google Open Source mentions (25)

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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
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What are some alternatives?

When comparing Google Open Source and Keras, you can also consider the following products

LaunchKit - Open Source - A popular suite of developer tools, now 100% open source.

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.

GitHub Sponsors - Get paid to build what you love on GitHub

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

Open Collective - Recurring funding for groups.

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

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