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Deepart.io VS Jupyter

Compare Deepart.io VS Jupyter and see what are their differences

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Deepart.io logo Deepart.io

Artificial intelligence turning your photos into art

Jupyter logo 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.
  • Deepart.io Landing page
    Landing page //
    2018-11-05
  • Jupyter Landing page
    Landing page //
    2023-06-22

Deepart.io features and specs

  • Artistic Transformation
    Deepart.io uses neural networks to transform photos into artwork, allowing users to turn their photos into masterpieces in the style of famous artists and art movements.
  • User-Friendly Interface
    The platform is easy to use, offering a straightforward interface that is accessible even for those with limited technical or artistic skills.
  • Creative Exploration
    The tool encourages creativity by allowing users to experiment with different artistic styles, providing an opportunity to explore and develop one's artistic sense.
  • Online Accessibility
    Being a web-based platform, Deepart.io allows users to access its features without needing to download software, making it convenient and accessible from any device with an internet connection.

Possible disadvantages of Deepart.io

  • Processing Time
    Art transformations can take a significant amount of time to process, especially during peak usage periods, which can be frustrating for users seeking immediate results.
  • Image Resolution
    The resolution of the final artwork may not be as high as expected, which can be a limitation for users looking to print or professionally use the transformed images.
  • Commercial Use Restrictions
    Art created on Deepart.io may have restrictions regarding commercial use, which could limit business applications or the ability to monetize the artwork.
  • Subscription Costs
    While the platform offers some free features, advanced options and higher resolution outputs may require a subscription or a one-time payment, which could be a barrier for some users.

Jupyter features and specs

  • Interactive Computing
    Jupyter allows real-time interaction with the data and code, providing immediate feedback and making it easier to experiment and iterate.
  • Rich Media Output
    It supports output in various formats including HTML, images, videos, LaTeX, and more, enhancing the ability to visualize and interpret results.
  • Language Agnostic
    Jupyter supports multiple programming languages through its kernel system (e.g., Python, R, Julia), allowing flexibility in the choice of tools.
  • Collaborative Features
    It enables collaboration through shared notebooks, version control, and platform integrations like GitHub.
  • Educational Tool
    Jupyter is widely used for teaching, thanks to its easy-to-use interface and ability to combine narrative text with code, making it ideal for assignments and tutorials.
  • Extensibility
    Jupyter is highly extensible with a large ecosystem of plugins and extensions available for various functionalities.

Possible disadvantages of Jupyter

  • Performance Issues
    For larger datasets and more complex computations, Jupyter can be slower compared to running scripts directly in a dedicated IDE.
  • Version Control Challenges
    Managing version control for Jupyter notebooks can be cumbersome, as they are not plain text files and include metadata that can make diffing and merging complex.
  • Resource Intensive
    Running Jupyter notebooks can be resource-intensive, especially when working with multiple large notebooks simultaneously.
  • Security Concerns
    Because Jupyter allows code execution in the browser, it can be a potential security risk if notebooks from untrusted sources are run without restrictions.
  • Dependency Management
    Managing dependencies and ensuring that the notebook runs consistently across different environments can be challenging.
  • Less Suitable for Production
    Jupyter is often considered more as a research and educational tool rather than a production environment; transitioning from a notebook to production code can require significant refactoring.

Deepart.io videos

+ Add

I AM AN ARTIST | Friday Rayday | DeepArt.io

Jupyter videos

+ Add

What is Jupyter Notebook?

More videos:

  • Tutorial - Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
  • Review - JupyterLab: The Next Generation Jupyter Web Interface

Category Popularity

0-100% (relative to Deepart.io and Jupyter)

Deepart.io

Jupyter

Digital Drawing And Painting
100 100%
Digital Drawing And Painting
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Image Editing
100 100%
Image Editing
0% 0
Data Dashboard
0 0%
Data Dashboard
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 Deepart.io and Jupyter

Deepart.io Reviews

We have no reviews of Deepart.io yet.
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Jupyter Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Once you install nteract, you can open your notebook without having to launch the Jupyter Notebook or visit the Jupyter Lab. The nteract environment is similar to Jupyter Notebook but with more control and the possibility of extension via libraries like Papermill (notebook parameterization), Scrapbook (saving your notebook’s data and photos), and Bookstore (versioning).
Source: lakefs.io
7 best Colab alternatives in 2023
JupyterLab is the next-generation user interface for Project Jupyter. Like Colab, it's an interactive development environment for working with notebooks, code, and data. However, JupyterLab offers more flexibility as it can be self-hosted, enabling users to use their own hardware resources. It also supports extensions for integrating other services, making it a highly...
Source: deepnote.com
12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing
Jupyter Notebook is a widely popular tool for data scientists to work on data science projects. This article reviews the top 12 alternatives to Jupyter Notebook that offer additional features and capabilities.
Source: noteable.io
15 data science tools to consider using in 2021
Jupyter Notebook's roots are in the programming language Python -- it originally was part of the IPython interactive toolkit open source project before being split off in 2014. The loose combination of Julia, Python and R gave Jupyter its name; along with supporting those three languages, Jupyter has modular kernels for dozens of others.
Source: searchbusinessanalytics.techtarget.com
Top 4 Python and Data Science IDEs for 2021 and Beyond
Yep — it’s the most popular IDE among data scientists. Jupyter Notebooks made interactivity a thing, and Jupyter Lab took the user experience to the next level. It’s a minimalistic IDE that does the essentials out of the box and provides options and hacks for more advanced use.
Source: towardsdatascience.com

Social recommendations and mentions

Based on our record, Jupyter seems to be a lot more popular than Deepart.io. While we know about 216 links to Jupyter, we've tracked only 19 mentions of Deepart.io. 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.

Deepart.io mentions (19)

  • Useful AI Tools for Blogging
    Quality visual content increases the appeal of a blog. Tools like Canva and DeepArt offer feature-rich options for creating and editing images. - Source: dev.to / 5 months ago
  • Schweizerische!
    I think deepart.io was the first free style-transfer tool. Source: almost 3 years ago
  • AI Stylized Render of a 3D Model I made. I was told you might like it here.
    Https://deepart.io is a bit weird sometimes. But if you fiddle with the settings for a bit it's really good. Source: almost 3 years ago
  • The picture doesn’t do this art justice. It’s soooo perfect!!
    I wouldn't. It's clearly one of the deep learning filters slapped over a screenshot. It's low effort and anyone can make it using something like this https://deepart.io/ something done by hand would look so much better. Source: about 3 years ago
  • ILPT Request: Ways to make pictures look handdrawn?
    Use an ai site like deepart.io, input the picture, and then an image of a drawing you want to recreate the style of. It basically recreates the image but in the style of the drawing. Source: over 3 years ago
View more

Jupyter mentions (216)

  • The 3 Best Python Frameworks To Build UIs for AI Apps
    Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / 2 months ago
  • LangChain: From Chains to Threads
    LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 3 months ago
  • Applied Artificial Intelligence & its role in an AGI World
    Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 4 months ago
  • Jupyter Notebook for Java
    Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 8 months ago
  • JIRA Analytics with Pandas
    One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / 11 months ago
View more

What are some alternatives?

When comparing Deepart.io and Jupyter, you can also consider the following products

Prisma - Art filters using artificial intelligence to transform your photos into classic artwork.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

Deep Dream Generator - Create inspiring visual content in a collaboration with our AI enabled tools.

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

Deep Art Effects - Deep Art Effects transforms your photos and videos into works of neural art using artistic style transfer of famous artists.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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