PyTorch vs scikit-learn vs TensorFlow

PyTorch

589
635
+ 1
18
scikit-learn

651
642
+ 1
28
TensorFlow

1.9K
2K
+ 1
72
Advice on PyTorch, scikit-learn, and TensorFlow
Adithya Shetty
Adithya Shetty
Student at PES UNIVERSITY · | 5 upvotes · 16.7K views
Needs advice
on
TensorFlowTensorFlowPyTorchPyTorch
and
KerasKeras

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

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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.

What is 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.
What companies use PyTorch?
What companies use scikit-learn?
What companies use TensorFlow?

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What tools integrate with PyTorch?
What tools integrate with scikit-learn?
What tools integrate with TensorFlow?

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What are some alternatives to PyTorch, scikit-learn, and TensorFlow?
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.
Chainer
It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.
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