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?
What is scikit-learn?
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What are the cons of using PyTorch?
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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—for instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA’s 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’s deep learning toolkit which makes it easier to start—and speed up—distributed deep learning projects with TensorFlow:
(Direct GitHub repo: https://github.com/uber/horovod)
I used PyTorch when i was working on an AI application, image classification using deep learning.
Machine Learning in EECS 445