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Lobe
Lobe

1
10
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0
TensorFlow.js
TensorFlow.js

57
118
+ 1
6
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Lobe vs TensorFlow.js: 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 TensorFlow.js? Machine Learning in JavaScript. Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API.

Lobe and TensorFlow.js belong to "Machine Learning Tools" category of the tech stack.

TensorFlow.js is an open source tool with 11.2K GitHub stars and 816 GitHub forks. Here's a link to TensorFlow.js's open source repository on GitHub.

- No public GitHub repository available -

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 TensorFlow.js?

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
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          What tools integrate with Lobe?
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          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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