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Keras

933
971
+ 1
14
NLTK

94
135
+ 1
0
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Keras vs NLTK: What are the differences?

Developers describe Keras as "Deep Learning library for Theano and TensorFlow". Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/. On the other hand, NLTK is detailed 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.

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

Keras is an open source tool with 43.2K GitHub stars and 16.5K GitHub forks. Here's a link to Keras's open source repository on GitHub.

According to the StackShare community, Keras has a broader approval, being mentioned in 70 company stacks & 257 developers stacks; compared to NLTK, which is listed in 15 company stacks and 17 developer stacks.

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Pros of Keras
Pros of NLTK
  • 5
    Easy and fast NN prototyping
  • 5
    Quality Documentation
  • 4
    Supports Tensorflow and Theano backends
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    Cons of Keras
    Cons of NLTK
    • 3
      Hard to debug
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      What is Keras?

      Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

      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.

      Need advice about which tool to choose?Ask the StackShare community!

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

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      What are some alternatives to Keras and NLTK?
      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.
      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.
      MXNet
      A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
      scikit-learn
      scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
      CUDA
      A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
      See all alternatives