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TensorFlow

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Wit.ai

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Pros of TensorFlow
Pros of Wit.ai
  • 29
    High Performance
  • 17
    Connect Research and Production
  • 14
    Deep Flexibility
  • 11
    Auto-Differentiation
  • 10
    True Portability
  • 4
    Powerful
  • 4
    High level abstraction
  • 4
    Easy to use
  • 3
    The interface to train is really easy
  • 2
    It´s free

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Cons of TensorFlow
Cons of Wit.ai
  • 9
    Hard
  • 6
    Hard to debug
  • 1
    Documentation not very helpful
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    - No public GitHub repository available -

    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.

    What is Wit.ai?

    Iti is an API that makes it very easy for developers to create applications or devices that you can talk to. Any app, or any device, like a smart watch, Google Glass, Nest, even a car, can stream audio to the Wit API, and get actionable data in return. We turn speech into actions. Think Twilio for Natural Language, with Stripe-level developer friendliness.

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    What companies use TensorFlow?
    What companies use Wit.ai?
    See which teams inside your own company are using TensorFlow or Wit.ai.
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    What tools integrate with TensorFlow?
    What tools integrate with Wit.ai?

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    Blog Posts

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    What are some alternatives to TensorFlow and Wit.ai?
    Theano
    Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
    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.
    OpenCV
    OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    See all alternatives