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Standard JS VS Keras

Compare Standard JS VS Keras and see what are their differences

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Standard JS logo Standard JS

DevOps, Build, Test, Deploy, and Code Review

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.
  • Standard JS Landing page
    Landing page //
    2023-08-29
  • Keras Landing page
    Landing page //
    2023-10-16

Standard JS features and specs

  • Zero Configuration
    Standard JS comes with a set of rules and configurations out of the box. This eliminates the need to set up a linting configuration file, saving developers time and reducing the cognitive load associated with decision-making.
  • Uniformity
    By enforcing a consistent style across projects, Standard JS helps to create a uniform codebase. This makes it easier for teams to read and understand each other's code, reducing onboarding time for new developers.
  • Community and Support
    As a popular style guide and linter, Standard JS has a robust community and extensive documentation. This support makes it easier for developers to find solutions to issues and to integrate Standard JS into their projects.
  • Less Distraction
    With pre-set rules, developers spend less time debating over coding styles and more time focusing on actual code logic and building functionality.

Possible disadvantages of Standard JS

  • Limited Customization
    Since Standard JS comes with a predefined set of rules, it offers limited flexibility for customization. Developers who prefer tailor-made configurations might find it restrictive.
  • Opinionated Rules
    Standard JS follows an opinionated approach to styling, which might not align with certain team or individual preferences. Some developers might find specific enforced styles disagreeable.
  • Compatibility Issues
    In some cases, Standard JS rules might conflict with pre-existing project configurations or other linters in the project, possibly causing friction during integration.
  • Learning Curve
    For developers new to Standard JS, there may be a learning curve as they acclimate to its specific rules and enforcement practices, particularly if they're used to other style guides.

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 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

Standard JS 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
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Category Popularity

0-100% (relative to Standard JS and Keras)

Standard JS

Keras

Code Coverage
100 100%
Code Coverage
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Code Analysis
100 100%
Code Analysis
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 Standard JS and Keras

Standard JS Reviews

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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 Standard JS. We know about 35 links to it since March 2021 and only 27 links to Standard JS. 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.

Standard JS mentions (27)

  • Mastering Code Quality: Setting Up ESLint with Standard JS in TypeScript Projects
    Sorry, I've gone too far. I'm not here to persuade you to use Standard JS. My intention is to provide information and guidance on configuring JavaScript Standard Style for your team, should you agree with me or have other reasons to choose it. - Source: dev.to / about 1 year ago
  • Why is Prettier rock solid?
    I picked up standard[1] a while back for this reason, I don't want to have to think about it. It works fine, I have no complaints (took me a while to get used to not using semi-colons but now I prefer it) Same reason I use `cargo fmt` as well. [1] https://standardjs.com/. - Source: Hacker News / over 1 year ago
  • My prepared repositories for hacktoberfest 23 - any contributions are welcomed 🚀
    A Thin JavaScript Document Storage with Middleware Stack. - Source: dev.to / over 1 year ago
  • Dumb question
    For example, if you use https://standardjs.com/ - it will error on your second code snippet and if you ask it for an autofix - it will transfer the minus sign to the first line. Source: over 2 years ago
  • Unleash the Power of Java: A JavaScript Developer's Guide to Best Practices in Java Development
    In comparison, JavaScript doesn't have a strict coding standard, although it does have widely accepted code style guides like the Airbnb JavaScript Style Guide and the JavaScript Standard Style. These guides provide recommendations for code formatting and naming conventions, but they are not as strictly enforced as the Java coding standard. - Source: dev.to / over 2 years ago
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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 1 month 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 / 12 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
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What are some alternatives?

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

Prettier - An opinionated code formatter

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.

ESLint - The fully pluggable JavaScript code quality tool

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

EditorConfig - EditorConfig is a file format and collection of text editor plugins for maintaining consistent coding styles between different editors and IDEs.

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

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