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Neptune

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TensorFlow

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75
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TensorFlow vs Neptune: What are the differences?

Developers describe TensorFlow as "Open Source Software Library for Machine Intelligence". 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. On the other hand, Neptune is detailed as "The most lightweight experiment tracking tool for machine learning". It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.

TensorFlow and Neptune can be categorized as "Machine Learning" tools.

TensorFlow is an open source tool with 141K GitHub stars and 79.7K GitHub forks. Here's a link to TensorFlow's open source repository on GitHub.

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Pros of Neptune
Pros of TensorFlow
    Be the first to leave a pro
    • 23
      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 Neptune
    Cons of TensorFlow
      Be the first to leave a con
      • 8
        Hard
      • 5
        Hard to debug
      • 1
        Documentation not very helpful

      Sign up to add or upvote consMake informed product decisions

      - No public GitHub repository available -

      What is Neptune?

      It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.

      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 companies use Neptune?
      What companies use TensorFlow?
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        What tools integrate with Neptune?
        What tools integrate with TensorFlow?

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        What are some alternatives to Neptune and TensorFlow?
        Neo4j
        Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
        Dgraph
        Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP.
        Saturn
        It is a web development framework written in F# which implements the server-side MVC pattern. Many of its components and concepts will seem familiar to anyone with experience in other web frameworks like Ruby on Rails or Python’s Django.
        Keras
        Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
        PyTorch
        PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
        See all alternatives
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        How much does TensorFlow cost?
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