Difference between TensorFlow and PyTorch
Both frameworks TensorFlow and PyTorch, are the top libraries of machine learning and developed in Python language. These are open-source neural-network library framework. TensorFlow is a software library for differential and dataflow programming needed for various kinds of tasks, but PyTorch is based on the Torch library.
Why we use TensorFlow?
TensorFlow is a library framework for machine learning applications. This framework is a mathematical library used mainly for numerical computation to applying the data from the graph. The edges of the graph can represent multidimensional data arrays, and nodes represent various accurate representations. It teaches neural networks about the mathematical symbol, image recognition, and partial differentiation and fully capable of running on multiple GPUs and CPUs. Its architecture is flexible.
This framework might also support C#, Haskell, Julia, Rust, Scala, Crystal, and OCami.
Why we use PyTorch?
PyTorch is a machine learning library which is applicable for an application like natural language processing. Pytorch is also appropriate for building various types of applications.
This library framework has two essential features:
The first feature of the library is the automatic differentiation for training and building of the deep neural network.
The second feature would be the computational tensor ability with support from high power GPU acceleration.
Pytorch has three modules of operations. Optimum Module, Auto grad Module, and nn Module. Each Module has its specific functions and applications.
For example, the Optimum Module is used for implementing various types of the algorithm for the development of the neural network. The nn Module is for defining all the complex low- level neural networks.e566
Comparison between TensorFlow and PyTorch
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