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Codeplace VS Keras

Compare Codeplace VS Keras and see what are their differences

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Codeplace logo Codeplace

Learn how to code by building real web apps

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.
  • Codeplace Landing page
    Landing page //
    2019-02-03
  • Keras Landing page
    Landing page //
    2023-10-16

Codeplace features and specs

  • Project-Based Learning
    Codeplace offers a project-based learning approach that allows users to build real-world projects, which helps in understanding practical applications of coding concepts.
  • Comprehensive Curriculum
    The platform provides a detailed and structured curriculum that covers various web development topics, including front-end and back-end technologies.
  • Step-by-Step Guidance
    Codeplace provides clear, step-by-step instructions for each project, making it easier for beginners to follow along and complete projects successfully.
  • Learn by Doing
    Codeplace emphasizes hands-on learning, which can enhance retention and understanding of programming skills through active practice.
  • Community Support
    Users can benefit from a community of learners and professionals, allowing them to exchange ideas, get help, and improve their learning experience.

Possible disadvantages of Codeplace

  • Subscription Based
    Access to Codeplace requires a subscription, which may not be affordable for all users, especially those who are looking for free learning resources.
  • Limited Language Options
    Compared to other platforms, Codeplace may offer fewer language options or technologies, focusing primarily on web development stacks.
  • Self-Paced Learning
    While beneficial for some, the self-paced learning model may lack the structured schedule that helps some learners stay disciplined and motivated.
  • Project-Centric
    The focus on project-based learning might not cover theoretical concepts as deeply, which could be a downside for learners seeking a more academic approach.
  • Dependent on Internet Access
    As an online platform, continuous internet access is necessary to access content, which may not be ideal for those with unreliable internet connectivity.

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.

Codeplace videos

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Codeplace | Build a Ruby on Rails Admin Panel using rails_admin gem

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 Codeplace and Keras)

Codeplace

Keras

Education
100 100%
Education
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Online Learning
100 100%
Online Learning
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 Codeplace and Keras

Codeplace Reviews

We have no reviews of Codeplace 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

Based on our record, Keras seems to be more popular. 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.

Codeplace mentions (0)

We have not tracked any mentions of Codeplace yet. Tracking of Codeplace recommendations started around Mar 2021.

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 / 9 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 / 6 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 / 7 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 / 11 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 Codeplace and Keras, you can also consider the following products

Code4Startup - Learn Ruby on Rails, Python, AngularJS, NodeJS, React, Ionic by cloning AirBnb , TaskRabbit, Tinder, Product Hunt, Fiverr and . more.

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.

GoSkills - GoSkills offers bite-sized business courses.

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

Bloc.io - Learn to code and become a web developer in Ruby on Rails, HTML, CSS, Javascript, and jQuery in Bloc's Intense Online Web Development Apprenticeship.

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

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