Jupyter Notebook vs. Keras

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Jupyter Notebook
Score 9.4 out of 10
N/A
Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning. It supports over 40 programming languages, and notebooks can be shared with others using email, Dropbox, GitHub and the Jupyter Notebook Viewer. It is used with JupyterLab, a web-based IDE for…N/A
Keras
Score 7.0 out of 10
N/A
Keras is a Python deep learning libraryN/A
Pricing
Jupyter NotebookKeras
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Jupyter NotebookKeras
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Jupyter NotebookKeras
Considered Both Products
Jupyter Notebook
Chose Jupyter Notebook
As a beginner I tried all of them but finally due to simple and user friendly interface I opted it. I also tried visual basic which is also good platform with versatility, however for basic need it is the best.
Chose Jupyter Notebook
Jupyter is very easy to understand and easy to use. And can also be used by a student, freelancer, small industries, big industries. Jupyter also provides you a tool to work with machine learning and artificial intelligence.
Chose Jupyter Notebook
Negligible or no cost, Highly efficient, effective, scalable , hasslefree
Chose Jupyter Notebook
Jupyter Notebook is very attractive platform for new developers to code and to learn programming and perform tasks as compared to other IDE. It has very well and easy visualization, interactive programming and sharing the live code and slideshow is very easy as compare to …
Chose Jupyter Notebook
Jupyter is still the most well known and widely used platform I've seen. Using it over other competition like Zeppelin simply because of its availability, and my familiarity with its functionality.
Chose Jupyter Notebook
Jupyter Notebook is unique in that it offers a flexible, lightweight, easy-to-replicate way of organizing your code in a visually intuitive fashion that can be exported in a number of formats. I've found that the broad functionalities available within the notebooks suit a lot …
Chose Jupyter Notebook
Well, so far Jupyter Notebook has been the better tool for me. It gives us more freedom & has more ability to train ML models & do the data visualization more efficiently. It's easier to operate & has a very simple-to-understand UI & with the support for taking data from …
Chose Jupyter Notebook
I have used PyCharm as well as Jupyter Notebook and for me, Jupyter wins almost every time. I really like its user-friend interface for someone who is new to python programming. The ability to run a big chunk of code part by part is a big game-changer for me. One thing I would …
Chose Jupyter Notebook
It should have cleaner support for multi-environment setup and should also increase the amount of features. Moreover, more support should be present for other programming languages. It should also have the option to set a specific location that opens up whenever I run command …
Chose Jupyter Notebook
Jupyter is easier to handle and user friendly.

We have free access to it and its cell by cell executing feature is amazing.
Chose Jupyter Notebook
Jupyter Notebook has a nicer interface than RStudio in our opinion and since most of our group is familiar with Jupyter Notebook it has made it a default choice. Overall the interactive programming as well as the easy visualizations, model deployment, and markdown made Jupyter …
Chose Jupyter Notebook
Jupyter Notebook is the core feature extended on by many commercial alternatives. The commercial alternatives have more feature integration with the rest of their portfolio. RStudio is another competitor for interactive and literate programming.

Chose Jupyter Notebook
haven't actually explored as I decided to use it on a friend 's recommendation.
Chose Jupyter Notebook
An interesting thing is that Jupyter Notebook is run on browser environments which may or may not be a positive feature according to cases. VS Code on [the] other hand doesn't use any interface and can run Jupyter Notebooks too. Sometimes my browser consumes too much RAM due to …
Chose Jupyter Notebook
With Jupyter Notebook besides doing data analysis and performing complex visualizations you can also write machine learning algorithms with a long list of libraries that it supports. You can make better predictions, observations etc. with it which can help you achieve better …
Chose Jupyter Notebook
I like Jupyter Notebook over the other two because it keeps my work more organized. It helps me to structure my workflow and the ability to run commands in chunks keeps me from being confused when coming back to the work after some time.
Chose Jupyter Notebook
I selected Jupyter Notebook because this is better integrated with the existing production systems than optional tools (for example, R). It is also commonly used tool within the scientist community.
Chose Jupyter Notebook
When I tried Zeppelin in 2017, it was still in initial versions, Jupyter was way ahead as of then. Zeppelin had limitations and I wasn't confident of it making progress as much as Jupyter.
Keras
Chose Keras
As Keras is the high level API, so using Keras, we don't have to be bothered by the low level TensorFlow complexity, and we can reduce a lot coding and testing efforts.
Chose Keras
For beginners, I always recommend starting with Keras, because it's really easy to use and learn at first. There is not much pre-requisite for this to start with.
Chose Keras
Keras is much easier to learn as compared to TensorFlow. It also has a lot of built-in functionality that makes it much better than the alternatives.
Chose Keras
Keras is a good point where you can learn lots of things and also have hands-on experience. There is not much comparison of Keras with Tensorlow, as Keras is a wrapper library which supports TensorFlow and Theano as backends for computation. But once you have enough knowledge …
Chose Keras
Keras is good to develop deep learning models. As compared to TensorFlow, it's easy to write code in Keras. You have more power with TensorFlow but also have a high error rate because you have to configure everything by your own. And as compared to MATLAB, I will always prefer …
Chose Keras
TensorFlow and Caffe are bit hard to learn but they give you power to implement everything by you own. But most of the time it is not required to implement our own algorithm, we can solve the problem with just using the already provided algorithms. As compared to TensorFlow and …
Features
Jupyter NotebookKeras
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Jupyter Notebook
9.0
Ratings
7% above category average
Keras
-
Ratings
Connect to Multiple Data Sources10.00 Ratings00 Ratings
Extend Existing Data Sources10.00 Ratings00 Ratings
Automatic Data Format Detection8.50 Ratings00 Ratings
MDM Integration7.40 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Jupyter Notebook
7.0
Ratings
18% below category average
Keras
-
Ratings
Visualization6.00 Ratings00 Ratings
Interactive Data Analysis8.00 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Jupyter Notebook
9.5
Ratings
15% above category average
Keras
-
Ratings
Interactive Data Cleaning and Enrichment10.00 Ratings00 Ratings
Data Transformations10.00 Ratings00 Ratings
Data Encryption8.50 Ratings00 Ratings
Built-in Processors9.30 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Jupyter Notebook
9.3
Ratings
10% above category average
Keras
-
Ratings
Multiple Model Development Languages and Tools10.00 Ratings00 Ratings
Automated Machine Learning9.20 Ratings00 Ratings
Single platform for multiple model development10.00 Ratings00 Ratings
Self-Service Model Delivery8.00 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Jupyter Notebook
10.0
Ratings
16% above category average
Keras
-
Ratings
Flexible Model Publishing Options10.00 Ratings00 Ratings
Security, Governance, and Cost Controls10.00 Ratings00 Ratings
Best Alternatives
Jupyter NotebookKeras
Small Businesses
IBM Watson Studio
IBM Watson Studio
Score 10.0 out of 10
InterSystems IRIS
InterSystems IRIS
Score 7.7 out of 10
Medium-sized Companies
Posit
Posit
Score 10.0 out of 10
Posit
Posit
Score 10.0 out of 10
Enterprises
Posit
Posit
Score 10.0 out of 10
Posit
Posit
Score 10.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Jupyter NotebookKeras
Likelihood to Recommend
10.0
(0 ratings)
8.1
(0 ratings)
Usability
10.0
(0 ratings)
7.7
(0 ratings)
Support Rating
9.0
(0 ratings)
8.2
(0 ratings)
User Testimonials
Jupyter NotebookKeras
Likelihood to Recommend
I would rate it 9/10 while recommending Jupyter Notebook as it offers me a wide range of functionality to operate. It is very well suited for someone who is new to python programming as the user interface helps you build code line by line. I personally have written multiple programs in Python using Jupyter Notebook as it helps me organize long code by breaking it in a structure. Also the ability to write comments using '#' helps a lot to a reader understand the code.
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I would recommend it for use when anyone wants to quickly develop a neural network. Or if a user is solving any machine learning problem that includes deep learning. And this kind of problem will be like image recognition, face recognition, doing some text analysis using deep learning which includes LSTM or some other algorithm.
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Pros
  • Coding and error correction line by line
  • Simple and Effectiveness
  • Easy to use for visualisation and presentation of code
  • Could be used at any place any time without hassle
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  • Implementing neural networks and deep learning models is easy with this.
  • Data processing is easy with Python and Keras. Keras helps a lot and has a good collection of functions to do data processing.
  • It has good integration with other devices like Android.
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Cons
  • Need more Hotkeys for creating a beautiful notebook. Sometimes we need to download other plugins which messes [with] its default settings.
  • Not as powerful as IDE, which sometimes makes [the] job difficult and allows duplicate code as it get confusing when the number of lines increases. Need a feature where [an] error comes if duplicate code is found or [if a] developer tries the same function name.
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  • I didn't face any issue so far.
  • The only thing, you can't modify everything in this. So it's not recommended for constructing highly optimised algorithms.
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Usability
Jupyter is highly simplistic. It took me about 5 mins to install and create my first "hello world" without having to look for help. The UI has minimalist options and is quite intuitive for anyone to become a pro in no time. The lightweight nature makes it even more likeable.
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The reason for giving this much rating. 1. It makes my job really easy and fast. 2. Strong community support. 3. Overall cost.
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Support Rating
I haven't had a need to contact support. However, all required help is out there in public forums.
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Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.
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Alternatives Considered
Jupyter Notebook is unique in that it offers a flexible, lightweight, easy-to-replicate way of organizing your code in a visually intuitive fashion that can be exported in a number of formats. I've found that the broad functionalities available within the notebooks suit a lot of needs I have for EDA, modeling, and data export that makes other software products fairly redundant.
Read full review
As Keras is the high level API, so using Keras, we don't have to be bothered by the low level TensorFlow complexity, and we can reduce a lot coding and testing efforts.
Read full review
Return on Investment
  • Positive impact: flexible implementation on any OS, for many common software languages
  • Positive impact: straightforward duplication for adaptation of workflows for other projects
  • Negative impact: sometimes encourages pigeonholing of data science work into notebooks versus extending code capability into software integration
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  • It helped me in learning the basic concept of deep learning by having hands-on experience.
  • It has helped us to implement our NN with very little time.
  • It doesn't give you the whole power to customize your neural network. If you want that then you have to shift to TensorFLow
Read full review
ScreenShots