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ML Kit

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ScalaNLP

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ML Kit vs ScalaNLP: What are the differences?

Developers describe ML Kit as "Machine learning for mobile developers (by Google)". ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package. On the other hand, ScalaNLP is detailed as "A suite of machine learning and numerical computing libraries". ScalaNLP is a suite of machine learning and numerical computing libraries.

ML Kit and ScalaNLP can be categorized as "Machine Learning" tools.

Some of the features offered by ML Kit are:

  • Image labeling - Identify objects, locations, activities, animal species, products, and more
  • Text recognition (OCR) - Recognize and extract text from images
  • Face detection - Detect faces and facial landmarks

On the other hand, ScalaNLP provides the following key features:

  • ScalaNLP is the umbrella project for several libraries:
  • Breeze is a set of libraries for machine learning and numerical computing
  • Epic is a high-performance statistical parser and structured prediction library

ScalaNLP is an open source tool with 2.91K GitHub stars and 674 GitHub forks. Here's a link to ScalaNLP's open source repository on GitHub.

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- No public GitHub repository available -

What is ML Kit?

ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.

What is ScalaNLP?

ScalaNLP is a suite of machine learning and numerical computing libraries.

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    What tools integrate with ML Kit?
    What tools integrate with ScalaNLP?
      No integrations found
      What are some alternatives to ML Kit and ScalaNLP?
      Tensorflow Lite
      It is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.
      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.
      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.
      scikit-learn
      scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
      See all alternatives