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Lobe
Lobe

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PyTorch

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Lobe vs PyTorch: 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 PyTorch? A deep learning framework that puts Python first. 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.

Lobe and PyTorch can be categorized as "Machine Learning" tools.

PyTorch is an open source tool with 29.6K GitHub stars and 7.18K GitHub forks. Here's a link to PyTorch'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 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.
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          What are some alternatives to Lobe and PyTorch?
          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/
          ML Kit
          ML Kit brings Google鈥檚 machine learning expertise to mobile developers in a powerful and easy-to-use package.
          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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          Decisions about Lobe and PyTorch
          Conor Myhrvold
          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber | 6 upvotes 434.9K views
          atUber TechnologiesUber Technologies
          PyTorch
          PyTorch
          Keras
          Keras
          TensorFlow
          TensorFlow

          Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

          At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

          TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

          Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

          https://eng.uber.com/horovod/

          (Direct GitHub repo: https://github.com/uber/horovod)

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          How developers use Lobe and PyTorch
          Avatar of Yonas B.
          Yonas B. uses PyTorchPyTorch

          I used PyTorch when i was working on an AI application, image classification using deep learning.

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