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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Pytorch

    Score9.4 out of 10
    N/APytorch is an open source machine learning (ML) framework boasting a rich ecosystem of tools and libraries that extend PyTorch and support development in computer vision, NLP and or that supports other ML goals.N/A

    Quantum Boost

    N/AN/AQuantum Boost is an advanced online platform that uses artificial intelligence to reach set targets through the fewest possible experiments. Key features: Faster than DoE: Quantum Boost uses AI algorithms to ensure targets are achieved in the fewest amount of experiments possible. Flexible project development: Ability to update project definitions without losing all the knowledge gained so far, unlike most DoE software. User-friendliness:…

    $95

    per month

    Pricing
    PytorchQuantum Boost
    Editions & Modules
    No answers on this topic
    Trial
    $0
    14 days
    Starter
    $95
    per month
    Enterprise
    Custom
    per year
    Offerings
    Pricing Offerings
    PytorchQuantum Boost
    Free Trial
    NoYes
    Free/Freemium Version
    NoYes
    Premium Consulting/Integration Services
    NoNo
    Entry-level Setup FeeNo setup feeNo setup fee
    Additional Details
    More Pricing Information
    Community Pulse
    PytorchQuantum Boost
    Considered Both Products
    Open Source
    No answer on this topic
    Quantum Boost Ltd
    No answer on this topic
    Key User Insights
    Would buy again
    100%
    Would buy again
    6 Answers
    No answers on this topic
    Delivers good value for the price
    100%
    Delivers good value for the price
    6 Answers
    No answers on this topic
    Happy with the feature set
    100%
    Happy with the feature set
    6 Answers
    No answers on this topic
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    5 Answers
    No answers on this topic
    Implementation went as expected
    100%
    Implementation went as expected
    5 Answers
    No answers on this topic
    Best Alternatives
    PytorchQuantum Boost
    Small Businesses
    TensorFlow
    Score7.6 out of 10
    TensorFlow
    Score7.6 out of 10
    Medium-sized Companies
    Google Cloud AI
    Score8.7 out of 10
    Google Cloud AI
    Score8.7 out of 10
    Enterprises
    Google Cloud AI
    Score8.7 out of 10
    Google Cloud AI
    Score8.7 out of 10
    All AlternativesView all alternativesView all alternatives
    User Ratings
    PytorchQuantum Boost
    Likelihood to Recommend
    9.0
    (6 ratings)
    -
    (0 ratings)
    Usability
    10.0
    (1 ratings)
    -
    (0 ratings)
    User Testimonials
    PytorchQuantum Boost
    Likelihood to Recommend
    Open Source
    They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.
    Incentivized
    Read full review
    Quantum Boost Ltd
    No answers on this topic
    Pros
    Open Source
    • flexibility
    • Clean code, close to the algorithm.
    • Fast
    • Handles GPUs, multiple GPUs on a single machine, CPUs, and Mac.
    • Versatile, can work efficiently on text/audio/image/tabular datasets.
    Incentivized
    Read full review
    Quantum Boost Ltd
    No answers on this topic
    Cons
    Open Source
    • Since pythonic if developing an app with pytorch as backend the response can be substantially slow and support is less compares to Tensorflow
    Incentivized
    Read full review
    Quantum Boost Ltd
    No answers on this topic
    Usability
    Open Source
    The big advantage of PyTorch is how close it is to the algorithm. Oftentimes, it is easier to read Pytorch code than a given paper directly. I particularly like the object-oriented approach in model definition; it makes things very clean and easy to teach to software engineers.
    Incentivized
    Read full review
    Quantum Boost Ltd
    No answers on this topic
    Alternatives Considered
    Open Source
    Pytorch is very, very simple compared to TensorFlow. Simple to install, less dependency issues, and very small learning curve. TensorFlow is very much optimised for robust deployment but very complicated to train simple models and play around with the loss functions. It needs a lot of juggling around with the documentation. The research community also prefers PyTorch, so it becomes easy to find solutions to most of the problems. Keras is very simple and good for learning ML / DL. But when going deep into research or building some product that requires a lot of tweaks and experimentation, Keras is not suitable for that. May be good for proving some hypotheses but not good for rigorous experimentation with complex models.
    Incentivized
    Read full review
    Quantum Boost Ltd
    No answers on this topic
    Return on Investment
    Open Source
    • The ability to make models as never before
    • Being able to control the bias of models was not done before the arrival of Pytorch in our company
    Incentivized
    Read full review
    Quantum Boost Ltd
    No answers on this topic
    ScreenShots

    Quantum Boost Screenshots

    Screenshot of Editing a project definitionScreenshot of Generating suggestionsScreenshot of Completed generation of suggestions with the probability of reaching targetsScreenshot of Adding a categorical factor to the organizationScreenshot of Editing the project spreadsheet for experimental valuesScreenshot of Analytics