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Materialize CSS VS Keras

Compare Materialize CSS VS Keras and see what are their differences

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Materialize CSS logo Materialize CSS

A modern responsive front-end framework based on Material Design

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.
  • Materialize CSS Landing page
    Landing page //
    2021-10-14
  • Keras Landing page
    Landing page //
    2023-10-16

Materialize CSS features and specs

  • Responsive Design
    Materialize CSS provides a responsive grid system, making it easier to create layouts that work well on various screen sizes and devices.
  • Material Design
    Adheres to Google's Material Design principles, offering a modern and professional look that is consistent across web applications.
  • Component Variety
    Offers a wide range of pre-built components, including forms, buttons, cards, and navigation bars, which helps speed up development.
  • Customization
    Provides options to customize components using Sass, making it flexible for specific design needs.
  • Built-in JavaScript
    Includes built-in JavaScript components like modals, carousels, and tabs, which enhance functionality without requiring additional libraries.

Possible disadvantages of Materialize CSS

  • Learning Curve
    Although it follows Material Design guidelines, there is a learning curve associated with understanding and implementing its various components and classes.
  • File Size
    Including the full library can make your project heavier, affecting load times, especially for users on slower connections.
  • Customization Complexity
    While customizable, deeply customizing or overriding default styles can sometimes be complicated and time-consuming.
  • Dependency on jQuery
    Some components still rely on jQuery, adding to the library size and potentially conflicting with modern JavaScript practices.
  • Community Support
    Compared to larger frameworks like Bootstrap, Materialize CSS has a smaller community, which can result in fewer third-party resources and plugins.

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 Materialize CSS

Overall verdict

  • Materialize CSS is a good choice if you're looking for a framework that emphasizes Material Design and comes with a sleek, modern interface. It's particularly useful for developers who want to quickly integrate Google's design philosophy without delving into custom styling or layout creation.

Why this product is good

  • Materialize CSS is a front-end framework based on Material Design principles developed by Google. It provides a comprehensive set of UI components that help developers create visually appealing and responsive web applications quickly. The framework includes a variety of pre-styled components like buttons, forms, cards, and much more, which adhere to the Material Design guidelines. This consistency in design components helps ensure a cohesive and professional look across web applications. Additionally, it simplifies responsive design with a grid system that's easy to implement, and it comes with excellent documentation and a supportive community.

Recommended for

    Materialize CSS is recommended for teams and developers who prefer Google's Material Design aesthetic, are building applications with a focus on rapid UI development, and value consistency and ease of use. It's also great for projects where a pre-existing UI library speeds up the development process, such as prototypes, admin dashboards, or smaller web applications. However, for highly customized UI components or non-Material Design projects, other frameworks might be more suitable.

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

Materialize CSS videos

+ Add

Build A Travel Agency Theme With Materialize CSS 1.0.0

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 Materialize CSS and Keras)

Materialize CSS

Keras

Design Tools
100 100%
Design Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
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 Materialize CSS and Keras

Materialize CSS Reviews

15 Top Bootstrap Alternatives For Frontend Developers in 2024
Material Design is a design language that combines classic principles of successful design with innovation and technology. One of the downsides of Materialize is that it does not support older versions of web browsers, and sometimes it does not handle certain nestings of components properly, resulting in an odd appearance.
Source: coursesity.com
9 Best Bootstrap Alternatives | Best Frontend Frameworks [2024]
Materialize is a modern responsive front-end framework based on the Material Design principles of Google. Material design is a design language created by Google, which combines traditional design methods with innovations and technology.
Source: hackr.io
Top 10 Best CSS Frameworks for Front-End Developers in 2022
Created by Google in 2014, Materialize is a responsive UI framework for websites and Android apps. It provides many ready-to-use components, classes, and starter templates. It is compatible with Sass and has a responsive layout based on Bootstrap’s 12-column grid format. The best part? Its extensive color palettes. So, if you want to work with Material Design (Google’s...
Source: hackr.io
15 Best CSS Frameworks: Professional Bootstrap and Foundation Alternatives
It’s specifically designed to help you develop faster using a standard template and customizable components. As the name suggests, Materialize is based on the basic principles of Material Design.
Source: www.webdesignerdepot.com
10 of the Best Bootstrap Alternatives
Materialize is a Design-inspired front-end framework that offers everything you need to create beautiful websites and apps. It includes a responsive grid system, CSS components, and animation libraries. Materialize is also easy to use and customizable, making it the perfect choice for any project. Furthermore, a single basic responsive framework across all phases minimizes...
Source: thetechhacker.com

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 Materialize CSS. We know about 35 links to it since March 2021 and only 26 links to Materialize CSS. 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.

Materialize CSS mentions (26)

  • Top 5 Best CSS Frameworks for Responsive Web Design in 2024
    Materialize is a modern CSS framework based on Google’s Material Design. It was created and designed by Google to provide a unified and consistent user interface across all its products. Materialize is focused on user experience as it integrates animations and components to provide feedback to users. - Source: dev.to / 8 months ago
  • Front-end Framework: Comparing Bootstrap, Foundation and Materialize
    Materialize was created by a team of developers at Google, inspired by the principles of Material Design. Material Design is a design language developed by Google that emphasizes tactile surfaces, realistic lighting, and bold, graphic interfaces. Materialize aims to bring these principles to web development by providing a framework with ready-to-use components and styles based on Material Design. - Source: dev.to / about 1 year ago
  • Took a web development job without much experience, am I doomed?
    If you wanna make it look nice use materialize css works great with Django templates. Source: about 2 years ago
  • Best 3 Bootstrap Alternatives
    You can also visit the Materialize website and GitHub repository which currently has garnered over 38k likes and has been forked over 4k times by developers. - Source: dev.to / about 2 years ago
  • All About Hacktober Fest 2022 You Need To Know
    This repository consists of files required to deploy a Web App or PWA created with Materialize Css. - Source: dev.to / over 2 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 / 27 days 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 Materialize CSS and Keras, you can also consider the following products

Bootstrap - Simple and flexible HTML, CSS, and JS for popular UI components and interactions

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.

Tailwind CSS - A utility-first CSS framework for rapidly building custom user interfaces.

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

Foundation - The most advanced responsive front-end framework in the world

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

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