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NLTK

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NLTK vs TensorFlow: What are the differences?

What is NLTK? It is a leading platform for building Python programs to work with human language data. It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.

What is TensorFlow? 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.

NLTK and TensorFlow can be primarily classified as "Machine Learning" tools.

Uber Technologies, 9GAG, and Postmates are some of the popular companies that use TensorFlow, whereas NLTK is used by Index.co, Athento, and King's Digital Lab. TensorFlow has a broader approval, being mentioned in 259 company stacks & 742 developers stacks; compared to NLTK, which is listed in 15 company stacks and 17 developer stacks.

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Pros of NLTK
Pros of TensorFlow
    Be the first to leave a pro
    • 25
      High Performance
    • 16
      Connect Research and Production
    • 13
      Deep Flexibility
    • 9
      True Portability
    • 9
      Auto-Differentiation
    • 2
      Easy to use
    • 2
      High level abstraction
    • 1
      Powerful

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    Cons of NLTK
    Cons of TensorFlow
      Be the first to leave a con
      • 9
        Hard
      • 6
        Hard to debug
      • 1
        Documentation not very helpful

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      What is NLTK?

      It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.

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

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      What are some alternatives to NLTK and TensorFlow?
      SpaCy
      It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.
      Gensim
      It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
      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.
      scikit-learn
      scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
      Keras
      Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
      See all alternatives