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PyTorch
PyTorch

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PyTorch vs scikit-learn: What are the differences?

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.

What is scikit-learn? Easy-to-use and general-purpose machine learning in Python. scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch and scikit-learn can be primarily classified as "Machine Learning" tools.

"Developer Friendly" is the top reason why over 2 developers like PyTorch, while over 14 developers mention "Scientific computing" as the leading cause for choosing scikit-learn.

PyTorch and scikit-learn are both open source tools. scikit-learn with 36K GitHub stars and 17.6K forks on GitHub appears to be more popular than PyTorch with 29.6K GitHub stars and 7.18K GitHub forks.

Repro, Home61, and MonkeyLearn are some of the popular companies that use scikit-learn, whereas PyTorch is used by Suggestic, cotobox, and Depop. scikit-learn has a broader approval, being mentioned in 71 company stacks & 40 developers stacks; compared to PyTorch, which is listed in 21 company stacks and 46 developer stacks.

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.

What is scikit-learn?

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
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    What are some alternatives to PyTorch and scikit-learn?
    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.
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    Caffe2
    Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.
    MXNet
    A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
    Torch
    It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
    See all alternatives
    Decisions about PyTorch and scikit-learn
    Conor Myhrvold
    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber | 6 upvotes 436.7K 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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    Reviews of PyTorch and scikit-learn
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    How developers use PyTorch and scikit-learn
    Avatar of Yonas B.
    Yonas B. uses PyTorchPyTorch

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

    Avatar of Eliana Abraham
    Eliana Abraham uses scikit-learnscikit-learn

    Machine Learning in EECS 445

    How much does PyTorch cost?
    How much does scikit-learn cost?
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