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Fritz
Fritz

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ML Kit
ML Kit

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Fritz vs ML Kit: What are the differences?

What is Fritz? Mobile machine learning made easy. Fritz is the end-to-end solution for on-device ML. Create ML-powered features in your app with ease and cross-platform support.

What is 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.

Fritz and ML Kit can be primarily classified as "Machine Learning" tools.

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What is Fritz?

Fritz is the end-to-end solution for on-device ML. Create ML-powered features in your app with ease and cross-platform support.

What is ML Kit?

ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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            What tools integrate with Fritz?
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                What are some alternatives to Fritz and ML Kit?
                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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