Jupyter Notebook vs. Keras

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Jupyter Notebook
Score 8.9 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 7.8 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
Features
Jupyter NotebookKeras
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Jupyter Notebook
8.5
21 Ratings
1% above category average
Keras
-
Ratings
Connect to Multiple Data Sources9.021 Ratings00 Ratings
Extend Existing Data Sources9.220 Ratings00 Ratings
Automatic Data Format Detection8.514 Ratings00 Ratings
MDM Integration7.415 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Jupyter Notebook
9.6
21 Ratings
13% above category average
Keras
-
Ratings
Visualization9.621 Ratings00 Ratings
Interactive Data Analysis9.621 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Jupyter Notebook
9.0
21 Ratings
9% above category average
Keras
-
Ratings
Interactive Data Cleaning and Enrichment9.320 Ratings00 Ratings
Data Transformations8.921 Ratings00 Ratings
Data Encryption8.514 Ratings00 Ratings
Built-in Processors9.314 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Jupyter Notebook
8.9
21 Ratings
5% above category average
Keras
-
Ratings
Multiple Model Development Languages and Tools9.020 Ratings00 Ratings
Automated Machine Learning9.218 Ratings00 Ratings
Single platform for multiple model development9.221 Ratings00 Ratings
Self-Service Model Delivery8.020 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Jupyter Notebook
8.8
19 Ratings
3% above category average
Keras
-
Ratings
Flexible Model Publishing Options8.819 Ratings00 Ratings
Security, Governance, and Cost Controls8.718 Ratings00 Ratings
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Jupyter NotebookKeras
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Medium-sized Companies
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Score 8.2 out of 10
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Score 9.1 out of 10
Enterprises
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Score 7.8 out of 10
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All AlternativesView all alternativesView all alternatives
User Ratings
Jupyter NotebookKeras
Likelihood to Recommend
8.4
(22 ratings)
8.1
(6 ratings)
Usability
10.0
(1 ratings)
7.7
(2 ratings)
Support Rating
9.0
(1 ratings)
8.2
(2 ratings)
User Testimonials
Jupyter NotebookKeras
Likelihood to Recommend
Open Source
I've created a number of daisy chain notebooks for different workflows, and every time, I create my workflows with other users in mind. Jupiter Notebook makes it very easy for me to outline my thought process in as granular a way as I want without using innumerable small. inline comments.
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Open Source
Keras is quite perfect, if the aim is to build the standard Deep Learning model, and materialize it to serve the real business use case, while it is not suitable if the purpose is for research and a lot of non-standard try out and customization are required, in that case either directly goes to low level TensorFlow API or Pytorch
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Pros
Open Source
  • Simple and elegant code writing ability. Easier to understand the code that way.
  • The ability to see the output after each step.
  • The ability to use ton of library functions in Python.
  • Easy-user friendly interface.
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Open Source
  • One of the reason to use Keras is that it is easy to use. Implementing neural network is very easy in this, with just one line of code we can add one layer in the neural network with all it's configurations.
  • It provides lot of inbuilt thing like cov2d, conv2D, maxPooling layers. So it makes fast development as you don't need to write everything on your own. It comes with lot of data processing libraries in it like one hot encoder which also makes your development easy and fast.
  • It also provides functionality to develop models on mobile device.
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Cons
Open Source
  • 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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Open Source
  • As it is a kind of wrapper library it won't allow you to modify everything of its backend
  • Unlike other deep learning libraries, it lacks a pre-defined trained model to use
  • Errors thrown are not always very useful for debugging. Sometimes it is difficult to know the root cause just with the logs
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Usability
Open Source
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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Open Source
I am giving this rating depending on my experience so far with Keras, I didn't face any issue far. I would like to recommend it to the new developers.
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Support Rating
Open Source
I haven't had a need to contact support. However, all required help is out there in public forums.
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Open Source
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
Open Source
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 business decisions and save cost to the company. It stacks up better as we know Python is more widely used than R in the industry and can be learnt easily. Unlike PyCharm jupyter notebooks can be used to make documentations and exported in a variety of formats.
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Open Source
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 Keras as it is easy and powerful as well.
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Return on Investment
Open Source
  • 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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Open Source
  • Easy and faster way to develop neural network.
  • It would be much better if it is available in Java.
  • It doesn't allow you to modify the internal things.
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