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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Notepad++

    Score9.2 out of 10
    N/ANotepad++ is a popular free and open source text editor available under the GPL license, featuring syntax highlighting and folding, auto-complete, multi-document management, and ac customizable GUI.N/A

    TensorFlow

    Score7.6 out of 10
    N/ATensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.N/A
    Pricing
    Notepad++TensorFlow
    Editions & Modules
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    Offerings
    Pricing Offerings
    Notepad++TensorFlow
    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
    Notepad++TensorFlow
    Considered Both Products
    Open Source
    No answer on this topic
    Open Source
    No answer on this topic
    Key User Insights
    Would buy again
    100%
    Would buy again
    41 Answers
    No answers on this topic
    Delivers good value for the price
    100%
    Delivers good value for the price
    41 Answers
    No answers on this topic
    Happy with the feature set
    100%
    Happy with the feature set
    41 Answers
    No answers on this topic
    Lived up to sales and marketing promises
    96%
    Lived up to sales and marketing promises
    26 Answers
    No answers on this topic
    Implementation went as expected
    100%
    Implementation went as expected
    30 Answers
    No answers on this topic
    Best Alternatives
    Notepad++TensorFlow
    Small Businesses
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    Vim
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    Google Cloud AI
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    Enterprises
    Vim
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    User Ratings
    Notepad++TensorFlow
    Likelihood to Recommend
    8.5
    (62 ratings)
    6.0
    (15 ratings)
    Likelihood to Renew
    10.0
    (1 ratings)
    -
    (0 ratings)
    Usability
    9.0
    (4 ratings)
    9.0
    (1 ratings)
    Support Rating
    8.1
    (15 ratings)
    9.1
    (2 ratings)
    Implementation Rating
    -
    (0 ratings)
    8.0
    (1 ratings)
    User Testimonials
    Notepad++TensorFlow
    Likelihood to Recommend
    Open Source
    well suited for 1) Coding and Development - Writing and editing code, Quick prototyping and testing of code snippets, Debugging and inspecting code using syntax highlighting and line numbering, 2) web development - Creating and editing HTML, CSS, JavaScript, and other web-related files .Managing and organizing web projects with multiple files and directories. Not suited for - 1) processing huge files 2) graphic designing 3) complex gui designs 3) Data Analysis and Manipulation - Editing and cleaning up text-based data files before importing them into analytical tools. Applying regular expressions to extract, transform, and manipulate data. 4) System Administration and IT - change system configuration file
    Incentivized
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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).
    Incentivized
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    Pros
    Open Source
    • Notepad++ allows us to keep open files in tabs. Like in a web browser, these tabs let us access these files quickly and easily. Furthermore, even if we forget to save the files when closing the program or shutting down the PC, Notepad++ retains them in the open tabs when we reopen it.
    • Notepad++ supports many different file types. We usually save our files created in Notepad as normal text files, but sometimes as JSON, PHP, and HTML files.
    • Notepad++ is lightweight and requires little resources. Using it is snappy and responsive.
    • The developer of Notepad++ frequently updates the software with bug fixes, performance improvements and new features.
    Incentivized
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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.
    Incentivized
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    Cons
    Open Source
    • UI looks a bit dated.
    • Sometimes the number of options are overwhelming and require a quick search to figure out where to locate a particular function.
    • Some way to do a diff between files would be great. Still need to resort to another paid app for that - unless it is a buried function I don't know about or there's a plugin for it.
    Incentivized
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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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    Likelihood to Renew
    Open Source
    I use it every day for 13 years already and it never disappointed me
    Incentivized
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    Open Source
    No answers on this topic
    Usability
    Open Source
    There are lot of features to talk about. Especially the usability is good. Everyone can easily to use and user-friendly. Can also update easily. Can also write and execute the programming languages like C, C++ etc. Encoding is also the major feature that helps me a lot and converter as well.
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    Open Source
    Support of multiple components and ease of development.
    Incentivized
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    Support Rating
    Open Source
    I haven't needed to utilize any support related to Notepad++. I guess this is a good thing because I found it to be quite intuitive. There are almost infinite features you can tweak and plugins you can download but I haven't had to do that because Notepad++ is really good right out of the box.
    Incentivized
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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.
    Incentivized
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    Implementation Rating
    Open Source
    No answers on this topic
    Open Source
    Use of cloud for better execution power is recommended.
    Incentivized
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    Alternatives Considered
    Open Source
    Notepad for Windows, Microsoft Word...LibreOffice Writer....I have used all of these for code writing and editing. Once again I like the universal feel of Notepad++. Basic Notepad, is just that, basic...and kind of clunky for what it is. This is a cool that I have installed on all my computers and also keep it on a thumb drive if I need it elsewhere.
    Incentivized
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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
    Incentivized
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    Return on Investment
    Open Source
    • Productivity has increased for developers.
    • It's free so instead of buying a piece of software, you can use this to replace many of them that may only specialize in one thing.
    • It gives our developers confidence knowing they have such a reliable, free tool at their disposal.
    Incentivized
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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.
    Incentivized
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