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ML5.js VS Keras

Compare ML5.js VS Keras and see what are their differences

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ML5.js logo ML5.js

Friendly machine learning for the web

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.
  • ML5.js Landing page
    Landing page //
    2021-10-12
  • Keras Landing page
    Landing page //
    2023-10-16

ML5.js features and specs

  • Ease of Use
    ml5.js is designed with simplicity in mind, making machine learning accessible to artists, creative coders, and students, even those without a robust background in AI or machine learning.
  • Browser-based
    Operates directly in the browser, eliminating the need for any additional setup or dependencies, which makes it highly compatible with web projects.
  • Pre-trained Models
    Includes a variety of pre-trained models for quick implementation of complex machine learning tasks like image classification, pose detection, and text generation.
  • Community and Documentation
    Strong community support and well-documented guides and examples help new users get started quickly and find solutions to common issues.
  • Integration with p5.js
    Integrates seamlessly with p5.js, a popular JavaScript library for creative coding, facilitating the development of interactive and visually engaging applications.

Possible disadvantages of ML5.js

  • Performance Limitations
    Since it runs in the browser, it may not be suitable for performance-intensive applications or those requiring real-time processing of large datasets.
  • Limited Customization
    While it offers pre-trained models, there is limited functionality for training new models from scratch compared to more comprehensive libraries like TensorFlow.js.
  • Dependency on Web Standards
    Depends on the performance and capabilities of the client's browser, which can vary significantly between different users and devices.
  • Size of Models
    Some pre-trained models can be quite large, which may affect loading times and performance on slower network connections or less powerful devices.
  • Scope
    Focused on high-level tasks and applications, which might not be sufficient for advanced machine learning requirements or niche functionalities outside its provided models.

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 ML5.js

Overall verdict

  • ML5.js is considered a good tool for web developers who want to incorporate machine learning into their projects without needing extensive knowledge of the underlying algorithms. It's particularly noted for its simplicity and ease of use.

Why this product is good

  • ML5.js is a high-level JavaScript library built on top of TensorFlow.js. It is designed to make machine learning accessible to web developers with minimal expertise in machine learning or deep learning. The library provides easy-to-use functions for tasks such as image and video classification, pose detection, text generation, and more. Its simplicity and ease of integration make it ideal for rapid prototyping and educational purposes.

Recommended for

    ML5.js is recommended for educators, beginners, artists, and developers who want to quickly implement machine learning models in web applications. It is also suitable for creative coding projects and interactive applications where simplicity and ease of use are important.

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

ML5.js videos

+ Add

ml5.js: Train Your Own Neural Network

More videos:

  • Review - ml5.js: Image Classification with MobileNet
  • Review - Image classification to gif with ML5.js | Vue.js Virtual Meetup

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

Category Popularity

0-100% (relative to ML5.js and Keras)

ML5.js

Keras

AI
100 100%
AI
0% 0
Data Science And Machine Learning
32 32%
Data Science And Machine Learning
68% 68
Developer Tools
100 100%
Developer Tools
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 ML5.js and Keras

ML5.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

Based on our record, Keras should be more popular than ML5.js. 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.

ML5.js mentions (10)

  • How AI is Transforming Front-End Development in 2025!
    Ml5.js: Built on top of TensorFlow.js, it provides a user-friendly interface for implementing machine learning in web applications.​. - Source: dev.to / about 2 months ago
  • Riffr - Create Photo Montages in the Browser with some ML Magic✨
    Important APIs - ml5 for in-browser detection, face-api that uses tensorflow-node to accelerate on-server detection. VueUse for a bunch of useful component tools like the QR Code generator. Yahoo's Gifshot for creating gif files in-browser etc. - Source: dev.to / over 2 years ago
  • Brain.js: GPU Accelerated Neural Networks in JavaScript
    See also: https://ml5js.org/ "The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.". - Source: Hacker News / almost 3 years ago
  • [Showoff Saturday] I made a captcha prototype that requires a banana
    I used ml5js.org , p5js.org and https://teachablemachine.withgoogle.com to train the Banana images. When you create a new image project on Teachable Machine, you can output the p5js and basically use it right out of the box - I customized js, css, and html from there. Source: over 3 years ago
  • My First 30 Days of 100 Days of Code.
    Going forward: I'll be 100% into JavaScript. You can use JavaScript in so many fields nowadays. Websites React, Mobile Apps React Native, Machine Learning TensorFlow & ML5, Desktop Applications Electron, and of course the backend Node as well. It's kind of a no-brainer. Of course, they all have specific languages that are better, but for now, JavaScript is a bit of a catch-all. - Source: dev.to / over 3 years ago
View more

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
View more

What are some alternatives?

When comparing ML5.js and Keras, you can also consider the following products

Amazon Machine Learning - Machine learning made easy for developers of any skill level

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.

Machine Learning Playground - Breathtaking visuals for learning ML techniques.

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

Apple Machine Learning Journal - A blog written by Apple engineers

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

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