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Keras VS Datalore

Compare Keras VS Datalore 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.

Datalore logo Datalore

Datalore is an interactive web-based workbook for data analysis, scientific exploration and visualization in Python.
  • Keras Landing page
    Landing page //
    2023-10-16
  • Datalore Landing page
    Landing page //
    2022-12-17

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.

Datalore features and specs

  • Integrated Development Environment
    Datalore provides a sophisticated and user-friendly IDE specifically tailored for data science, offering features such as auto-completion, version control, and easy collaboration.
  • Collaboration Tools
    Allows multiple users to work simultaneously on the same notebook, enabling efficient teamwork with features like comments and real-time synchronization.
  • Built-in Libraries and Tools
    Includes pre-installed libraries essential for data analysis, machine learning, and visualization, which saves time compared to setting up environments from scratch.
  • Scalability
    Datalore can be connected to cloud computing resources, allowing users to scale their computations as needed, which is beneficial for handling large datasets.
  • JetBrains Ecosystem
    As a product of JetBrains, Datalore integrates well with other JetBrains tools and benefits from ongoing development and dedicated support.

Possible disadvantages of Datalore

  • Pricing Model
    While a free tier is available, advanced features and increased resource access require a paid subscription, which might be a barrier for some users.
  • Learning Curve
    Users not familiar with JetBrains' interfaces might find the transition to Datalore a bit challenging compared to more widely-used platforms like Jupyter.
  • Internet Dependency
    As a cloud-based service, a stable internet connection is necessary, which can be inconvenient or limiting in environments with poor connectivity.
  • Limited Offline Functionality
    Unlike local installations of Jupyter or RStudio, Datalore's functionality is limited offline, potentially restricting use cases where offline access is needed.
  • Resource Limitations on Free Tier
    The free tier has restrictions on computation time and resources, which may not be suitable for extensive or resource-intensive data analysis projects.

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

+ 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

Datalore videos

+ Add

Star Trek: TNG Review - 1x13 Datalore | Reverse Angle

More videos:

  • Tutorial - Getting started with Datalore: online Jupyter notebook tutorial
  • Tutorial - Visualization Tutorial With Pyplot in Datalore by JetBrains

Category Popularity

0-100% (relative to Keras and Datalore)

Keras

Datalore

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Machine Learning
75 75%
Machine Learning
25% 25
Data Science Tools
100 100%
Data Science Tools
0% 0
Data Science Notebooks
0 0%
Data Science Notebooks
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 Datalore

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

Datalore Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
One of JetBrains Datalore’s advantages is its interaction with the JetBrains ecosystem of tools, which includes IDEs like PyCharm and IntelliJ. That’s also why the tool is primarily aimed at ecosystem users.
Source: lakefs.io
12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing
JetBrains Datalore is a cloud-based data science platform that offers many of the same features as Jupyter Notebooks, as well as a number of additional capabilities. It supports a wide variety of programming languages, including Python, R, and SQL, and provides access to powerful hardware resources, including GPUs. One of the main advantages of JetBrains Datalore is its...
Source: noteable.io

Social recommendations and mentions

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

Datalore mentions (10)

  • Plotting Financial Data in Kotlin with Kandy
    For working with datasets (loading and processing), I use Kotlin DataFrame. It is a library designed for working with structured in-memory data, such as tabular or JSON. It offers convenient storage, manipulation, and data analysis with a convenient, typesafe, readable API. With features for data initialization and operations like filtering, sorting, and integration, Kotlin DataFrame is a powerful tool for data... - Source: dev.to / about 1 year ago
  • A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev
    Datalore - Python notebooks by Jetbrains. Includes 10 GB of storage and 120 hours of runtime each month. - Source: dev.to / over 1 year ago
  • Best online course to actually learn to use Python
    Last 1/3 of course sections: More of the same really, thought I had sections where I had to install earlier iterations of Python due to incompatible libraries in some of the course sections. As ever, student comments & furious Stack Overflow searches were helpful. Also, Jupyter notebooks are introduced in this part of the course. As I'm using the Community Edition of Pycharm for the course AND the free versions... Source: over 2 years ago
  • A new take on a Jupyter interface
    - Do you know about https://datalore.jetbrains.com/? They seem to have this cool thing where you can rewind the state of the notebook using CRIU. I don't know how well this works in practice but I think it could help with experiment management, debugging and getting code to production. Source: over 2 years ago
  • New Jupyter Notebook competition
    Have you looked at Datalore, https://datalore.jetbrains.com/. Source: about 3 years ago
View more

What are some alternatives?

When comparing Keras and Datalore, 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.

Colaboratory - Free Jupyter notebook environment in the cloud.

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

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

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

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

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