Software Alternatives, Accelerators & Startups

Keras VS GitHub Gist

Compare Keras VS GitHub Gist 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.

GitHub Gist logo GitHub Gist

Gist is a simple way to share snippets and pastes with others.
  • Keras Landing page
    Landing page //
    2023-10-16
  • GitHub Gist Landing page
    Landing page //
    2022-07-28

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.

GitHub Gist features and specs

  • Ease of Use
    GitHub Gist provides a simple interface for creating and sharing code snippets or textual information. Users can quickly create new gists without needing to set up a full repository.
  • Version Control
    Each gist benefits from built-in version control, allowing users to track changes and roll back to previous versions if necessary.
  • Collaboration
    Gists can be shared with others easily, and collaborators can comment on, suggest changes, and fork the gist for further modification, making it a good tool for code reviews and quick sharing.
  • Embed and Share
    Gists can be embedded into websites and blogs, making it easy to share code in a readable and aesthetically pleasing way.
  • Public or Private
    Users have the option to create public or secret gists, offering flexibility in terms of visibility and accessibility.

Possible disadvantages of GitHub Gist

  • Limited Features
    Gists are not full-fledged repositories and lack many features that GitHub repositories offer, such as project management tools and issue tracking.
  • Search and Organization
    Managing and finding gists can become challenging as there is no internal folder structure or advanced search capability to organize them effectively.
  • Security
    While gists can be made private, they are still accessible by anyone who has the URL. They do not provide the same level of access control as private GitHub repositories.
  • Limited Collaboration
    While gists support basic collaboration through comments and forks, they do not offer the comprehensive collaboration tools available in full GitHub repositories, such as detailed pull requests and issue tracking.
  • File Size Limitation
    Gists have a file size limit, making them unsuitable for larger files or projects. This limits their use for anything beyond simple or small code snippets.

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

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

GitHub Gist videos

Deploy Website using GitHub Pages in less than 10 mins

Category Popularity

0-100% (relative to Keras and GitHub Gist)
Data Science And Machine Learning
Design Playground
0 0%
100% 100
Data Science Tools
100 100%
0% 0
JavaScript
0 0%
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 GitHub Gist

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...

GitHub Gist Reviews

We have no reviews of GitHub Gist yet.
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Social recommendations and mentions

Based on our record, Keras should be more popular than GitHub Gist. 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 / 9 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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GitHub Gist mentions (8)

  • Help…I’m slightly embarrassed to post this…but could anyone look at my profile and let me know if there are any “newbie red flags”. I’ve fallen in love with Python and decided to post projects from the classes I’ve taken. I’ve got more advanced projects to post and still have some project cleaning!
    If you are learning things, you could also create github gists. That way your repos will only be coding related, while you can create tutorials / work exercises in gists. Source: over 2 years ago
  • Best Practice for keeping a library of code/functions to reuse in future projects
    I use Github, both for full repos and for short gists. Source: about 3 years ago
  • Flutter Challenges: Challenge 02
    On the other hand, shared DartPads are just gists on GitHub so theoretically they can include code that works with different packages. Of course, such gists will not compile in DartPad and will be displayed as having errors :(. Source: over 3 years ago
  • Best way to make notes about coding?
    Perhaps github gists? https://gist.github.com/discover. Source: over 3 years ago
  • Some information that may be useful on the *nature of the problem* posed by the pandemic and SARS-cov-2 virus
    I looked at Github gists, but they are focused in displaying the markdown sourcecode (so e.g. Hyperlinks won't be clickable [1] ). Options just don't seem to be focused on simply hosting PDFs/information with clickable references. Source: over 3 years ago
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What are some alternatives?

When comparing Keras and GitHub Gist, 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.

Pastebin.com - Pastebin.com is a website where you can store text for a certain period of time.

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

PrivateBin - PrivateBin is a minimalist, open source online pastebin where the server has zero knowledge of...

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

hastebin - Pad editor for source code.