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NLTK
NLTK

36
14
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0
Paperspace
Paperspace

1
0
+ 1
0
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NLTK vs Paperspace: What are the differences?

Developers describe NLTK as "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. On the other hand, Paperspace is detailed as "The way to access and manage limitless computing power in the cloud". It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.

NLTK and Paperspace belong to "Machine Learning Tools" category of the tech stack.

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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 Paperspace?

It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.
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            What are some alternatives to NLTK and Paperspace?
            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.
            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.
            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/
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