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Lobe
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Lobe vs numericaal: What are the differences?

What is Lobe? Deep learning made simple. An easy-to-use visual tool that lets you build custom deep learning models, quickly train them, and ship them directly in your app without writing any code.

What is numericaal? Machine learning for mobile & IoT made easy. numericaal automates model optimization and management so you can focus on data and training.

Lobe and numericaal can be primarily classified as "Machine Learning" tools.

Some of the features offered by Lobe are:

  • Build - Drag in your training data and Lobe automatically builds you a custom deep learning model. Then refine your model by adjusting settings and connecting pre-trained building blocks.
  • Train - Monitor training progress in real-time with interactive charts and test results that update live as your model improves. Cloud training lets you get results quickly, without slowing down your computer.
  • Ship - Export your trained model to TensorFlow or CoreML and run it directly in your app on iOS and Android. Or use the easy-to-use Lobe Developer API and run your model remotely over the air.

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 Lobe?

An easy-to-use visual tool that lets you build custom deep learning models, quickly train them, and ship them directly in your app without writing any code.

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 Lobe 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.
                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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