Jupyter Notebook vs. TensorFlow

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Jupyter Notebook
Score 8.7 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
TensorFlow
Score 9.0 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.N/A
Pricing
Jupyter NotebookTensorFlow
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Jupyter NotebookTensorFlow
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 NotebookTensorFlow
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Jupyter Notebook
8.5
21 Ratings
1% above category average
TensorFlow
-
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
TensorFlow
-
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
TensorFlow
-
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
TensorFlow
-
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
TensorFlow
-
Ratings
Flexible Model Publishing Options8.819 Ratings00 Ratings
Security, Governance, and Cost Controls8.718 Ratings00 Ratings
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Jupyter NotebookTensorFlow
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Score 8.3 out of 10
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Score 9.1 out of 10
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User Ratings
Jupyter NotebookTensorFlow
Likelihood to Recommend
8.4
(22 ratings)
8.5
(14 ratings)
Usability
10.0
(1 ratings)
9.0
(1 ratings)
Support Rating
9.0
(1 ratings)
9.1
(2 ratings)
Implementation Rating
-
(0 ratings)
8.0
(1 ratings)
User Testimonials
Jupyter NotebookTensorFlow
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
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
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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
  • A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
  • Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
  • Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.
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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
  • RNNs are still a bit lacking, compared to Theano.
  • Cannot handle sequence inputs
  • Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
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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
Support of multiple components and ease of development.
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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
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
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Implementation Rating
Open Source
No answers on this topic
Open Source
Use of cloud for better execution power is recommended.
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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 built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
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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
  • Learning is s bit difficult takes lot of time.
  • Developing or implementing the whole neural network is time consuming with this, as you have to write everything.
  • Once you have learned this, it make your job very easy of getting the good result.
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