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ML Kit
ML Kit

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numericaal
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ML Kit vs numericaal: What are the differences?

ML Kit: 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; numericaal: Machine learning for mobile & IoT made easy. numericaal automates model optimization and management so you can focus on data and training.

ML Kit and numericaal belong to "Machine Learning Tools" category of the tech stack.

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, numericaal provides the following key features:

  • MODEL RESOURCE OPTIMIZATION - We automatically run multiple toolchains to give you the best speed, power and memory tradeoff on every model change.
  • CROSS-PLATFORM MODEL ANALYTICS - We measure on-device speed and power usage to help you evaluate and compare models across hardware platforms.
  • BOTTLENECK IDENTIFICATION - We help you pinpoint performance bottlenecks and focus your model optimization on layers that matter the most.
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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 numericaal?

numericaal automates model optimization and management so you can focus on data and training.
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                What are some alternatives to ML Kit and numericaal?
                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/
                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.
                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.
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