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Pros of SciPy
Pros of TensorFlow
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    • 32
      High Performance
    • 19
      Connect Research and Production
    • 16
      Deep Flexibility
    • 12
    • 11
      True Portability
    • 6
      Easy to use
    • 5
      High level abstraction
    • 5
    • 2
      Is orange

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    Cons of SciPy
    Cons of TensorFlow
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      • 9
      • 6
        Hard to debug
      • 2
        Documentation not very helpful

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      What is 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.

      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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      Blog Posts

      Dec 4 2019 at 8:01PM


      What are some alternatives to SciPy and TensorFlow?
      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.
      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.
      scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
      A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
      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.
      See all alternatives