Mckenzie  Osiki

Mckenzie Osiki


Tensorflow vs. PyTorch: A Detailed Comparison

Tensorflow vs. PyTorch: A Detailed Comparison

Deep learning is a common technique of solving real-world problems using human-like computers.

Deep learning tasks involve the creation of special brain-like architectures known as artificial neural networks.

To help develop such architectures, various Python frameworks for performing deep learning tasks have been developed, making it easier to build and train diversified artificial neural networks.

The two most popular frameworks for performing deep learning tasks are TensorFlow and PyTorch.

If you’ve ever done a deep learning task, you must have heard about the above terms.

Maybe, you’ve been trying to figure out which one to choose, or you’re just starting your deep learning journey and you’re asking yourself this question…

“Which one is better?”

This forms the subject of discussion in this tutorial.

I’ll be resolving all your doubts about these two popular frameworks and help you choose the one that suits your needs.

Table of Contents

You can skip to a specific section of this tutorial using the table of contents below:

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Tensorflow vs. PyTorch: A Detailed Comparison
Rohan Paul

Rohan Paul


Deep Learning with TensorFlow

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How PyTorch Is Challenging TensorFlow Lately

  • PyTorch gives our researchers unprecedented flexibility in designing their models and running their experiments.

Google’s TensorFlow and Facebook’s PyTorch are the most popular machine learning frameworks. The former has a two-year head start over PyTorch (released in 2016). TensorFlow’s popularity reportedly declined after PyTorch bursted into the scene. However, Google released a more user-friendly TensorFlow 2.0 in January 2019 to recover lost ground.

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Interest over time for TensorFlow (top) and PyTorch (bottom) in India (Credit: Google Trends)

PyTorch–a framework for deep learning that integrates with important Python add-ons like NumPy and data-science tasks that require faster GPU processing–made some recent additions:

  • Enterprise support**: **After taking over the Windows 10 PyTorch library from Facebook to boost GPU-accelerated machine learning training on Windows 10’s Subsystem for Linux(WSL), Microsoft recently added enterprise support for PyTorch AI on Azure to give PyTorch users a more reliable production experience. “This new enterprise-level offering by Microsoft closes an important gap. PyTorch gives our researchers unprecedented flexibility in designing their models and running their experiments,” Jeremy Jancsary, a senior principal research scientist at Nuance, said.
  • PyTorchVideois a deep learning library for video understanding unveiled by Facebook AI recently. The source code is available on GitHub. With this, Facebook aims to support researchers develop cutting-edge machine learning models and tools. These models can enhance video understanding capabilities along with providing a unified repository of reproducible and efficient video understanding components for research and production applications.
  • PyTorch Profiler: In April this year, PyTorch announced its new performance debug profiler, PyTorch Profiler, along with its 1.8.1 version release. The new tool enables accurate and efficient performance analysis in large scale deep learning models.

#opinions #deep learning frameworks #machine learning pytorch #open-source frameworks #pytorch #tensorflow #tensorflow 2.0

Gunjan  Khaitan

Gunjan Khaitan


Keras vs TensorFlow vs Pytorch | Deep Learning Frameworks Comparison 2021

With the Deep Learning scene being dominated by three main frameworks, it is very easy to get confused on which one to use? In this video on Keras vs Tensorflow vs Pytorch, we will clear all your doubts on which framework is better and which framework should be used by beginners, intermediates and professionals.

The topics covered in this video are :

  • 00:00:00 What is Keras, Tensorflow and Pytorch?
  • 00:05:27 Differences beteen Keras, tensorflow and Pytorch
  • 00:11:46 Which framework should you use?

#keras #tensorflow #pytorch #deep-learning

Justyn  Ortiz

Justyn Ortiz


Guide to Conda for TensorFlow and PyTorch

Learn how to set up anaconda environments for different versions of CUDA, TensorFlow, and PyTorch

It’s a real shame that the first experience that most people have with deep learning is having to spend days trying to figure out why the model they downloaded off of GitHub just… won’t… run….

Dependency issues are incredibly common when trying to run an off-the-shelf model. The most problematic of which is needing to have the correct version of CUDA for TensorFlow. TensorFlow has been prominent for a number of years meaning that even new models that are released could use an old version of TensorFlow. This wouldn’t be an issue except that it feels like every version of TensorFlow needs a specific version of CUDA where anything else is incompatible. Sadly, installing multiple versions of CUDA on the same machine can be a real pain!

#machine-learning #pytorch #tensorflow #pytorch

Pytorch vs Tensorflow vs Keras | Deep Learning Tutorial (Tensorflow, Keras & Python)

We will go over what is the difference between pytorch, tensorflow and keras in this video. Pytorch and Tensorflow are two most popular deep learning frameworks. Pytorch is by facebook and Tensorflow is by Google. Keras is not a full fledge deep learning framework, it is just a wrapper around Tensorflow that provides some convenient APIs.

#pytorch #tensorflow #keras #python #deep-learning