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NumPy

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TensorFlow

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76
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Pros of NumPy
Pros of TensorFlow
  • 6
    Great for data analysis
  • 24
    High Performance
  • 16
    Connect Research and Production
  • 13
    Deep Flexibility
  • 9
    Auto-Differentiation
  • 9
    True Portability
  • 2
    Easy to use
  • 2
    High level abstraction
  • 1
    Powerful

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Cons of NumPy
Cons of TensorFlow
    Be the first to leave a con
    • 8
      Hard
    • 5
      Hard to debug
    • 1
      Documentation not very helpful

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    What is NumPy?

    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

    What is TensorFlow?

    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

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    What tools integrate with TensorFlow?

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    Blog Posts

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    Dec 4 2019 at 8:01PM

    Pinterest

    +4
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    +42
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    38951
    What are some alternatives to NumPy and TensorFlow?
    Pandas
    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
    MATLAB
    Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.
    R Language
    R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
    SciPy
    Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
    Panda
    Panda is a cloud-based platform that provides video and audio encoding infrastructure. It features lightning fast encoding, and broad support for a huge number of video and audio codecs. You can upload to Panda either from your own web application using our REST API, or by utilizing our easy to use web interface.<br>
    See all alternatives
    How developers use NumPy and TensorFlow
    Vital Labs, Inc. uses
    NumPy

    We utilize NumPy, SciPy, Pandas, and iPython Notebooks to power our analysis and analytics tools.

    Taylor Host uses
    TensorFlow

    Pilot integration for retraining.

    GadgetSteve uses
    NumPy

    Fast Numeric Processing

    Nough You uses
    NumPy

    Fast array operations.

    BobStein uses
    NumPy

    big data analysis

    Andrea Catalucci uses
    NumPy

    Number crunching