Top Deep Learning Frameworks in 2020: PyTorch vs TensorFlow. This article outlines five factors to help you compare these two major deep learning frameworks; PyTorch and TensorFlow.

Deep learning is a sub-branch of machine learning. The unique aspect of Deep Learning is the accuracy and efficiency it brings to the table. When trained with a vast amount of data, Deep Learning systems can match, and even exceed, the cognitive powers of the human brain. How do the two top deep learning frameworks, i.e., PyTorch and TensorFlow, compare?

This article outlines five factors to help you compare these two major deep learning frameworks.

Tensorflow is basically a programming language that is embedded within Python, as Sorrow Beaver notes. Tensorflow's code gets 'compiled' into a graph by Python. It is then run by the TensorFlow execution engine. Pytorch, on the other hand, is essentially a GPU enabled drop-in replacement for NumPy that is equipped with a higher-level functionality to build and train deep neural networks.

With Pytorch, the code executes very fast, it is very efficient, and you will require no new concepts to learn. Tensorflow, on the other hand, requires concepts such as placeholders, Variable scoping as well as sessions.

Pytorch has a dynamic process of creating a graph. Graphs on PyTorch can be built by interpreting a line of code corresponding to the particular aspect of a graph.

Tensorflow, on the other hand, has a static process of graph creation that involves graphs going through compilation and running on the execution engine.

Pytorch code will use the standard Python debugger, unlike TensorFlow, where you will need to learn the TF debugger and request the variables from the session for inspection.

Tensorflow supports features such as:

- Fast Fourier transforms
- Checking a tensor for NaN and infinity
- Flipping a tensor along a dimension

These are features that Pytorch doesn't have, but as it grows in popularity, the gap will definitely be bridged.

When comparing the two frameworks in serialization, TensorFlow's graph can be saved as a protocol buffer, which includes operations and parameters. The TensorFlow graph can then be loaded in other programming languages, such as Java and C++. This is important, especially for deployment stacks, where Python is not an option.

Pytorch, on the other hand, has a simple API that can either pickle the entire class or save all weights of a model.

All in all, saving and loading models are simplified in these two frameworks.

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Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc

We will go through some of the popular deep learning frameworks like Tensorflow, MxNet, DL4j, PyTorch and CNTK so you can choose which one is best for your project. Deep Learning is a branch of Machine Learning. Though machine learning has various algorithms, the most powerful are neural networks. To build these neural networks, we use different frameworks like Tensorflow, CNTK, and MxNet.

Setting up your PC/Workstation for Deep Learning: Tensorflow and PyTorch — Windows. By far the easiest way to configure your new (or not so new) rig to crunch some neurons.

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