Keras vs PyTorch Last Updated : 19 Jan, 2023 Summarize Comments Improve Suggest changes Share Like Article Like Report Keras and PyTorch are two of the most powerful open-source machine learning libraries. Keras is a python based open-source library used in deep learning (for neural networks).It can run on top of TensorFlow, Microsoft CNTK or Theano. It is very simple to understand and use, and suitable for fast experimentation. Keras models can be run both on CPU as well as GPU. PyTorch is an open-source machine learning library which was developed by Facebook's AI Research Group. It can be integrated with Python and C++. It is popular because of its efficient memory usage and the ability to debug neural networks easily. Let's see the differences between Keras and PyTorch. S.NoKerasPyTorch1.Keras was released in March 2015.While PyTorch was released in October 2016.2.Keras has a high level API.While PyTorch has a low level API.3.Keras is comparatively slower in speed.While PyTorch has a higher speed than Keras, suitable for high performance.4.Keras has a simple architecture,making it more readable and easy to use.While PyTorch has very low readability due to a complex architecture.5.Keras has a smaller community support.While PyTorch has a stronger community support.6.Keras is mostly used for small datasets due to its slow speed.While PyTorch is preferred for large datasets and high performance.7.Debugging in Keras is difficult due to presence of computational junk.While debugging in PyTorch is easier and faster.8.Keras provides static computation graphs.While PyTorch provides dynamic computation graphs.9.Backend for Keras include:TensorFlow, Theano and Microsoft CNTK backend.While PyTorch has no backend implementation. Comment More infoAdvertise with us Next Article Keras vs PyTorch S sam816g Follow Improve Article Tags : Machine Learning python Practice Tags : Machine Learningpython Similar Reads Tensor Operations in PyTorch In this article, we will discuss tensor operations in PyTorch. PyTorch is a scientific package used to perform operations on the given data like tensor in python. A Tensor is a collection of data like a numpy array. We can create a tensor using the tensor function: Syntax: torch.tensor([[[element1,e 5 min read Speed up Algorithms in Pytorch PyTorch is a powerful open-source machine learning framework that allows you to develop and train deep learning models. However, as the size and complexity of your models grow, the time it takes to train them can become prohibitive. In this article, we will explore some techniques to speed up the al 5 min read PyTorch v/s Tensorflow Over the past few decades, deep learning has made astonishing progress in the field of Artificial Intelligence. Several frameworks can get you started with Deep Learning. But selecting the right one for your project is a difficult task. While there are a lot of frameworks to pick from, PyTorch and T 3 min read Computational Graph in PyTorch PyTorch is a popular open-source machine learning library for developing deep learning models. It provides a wide range of functions for building complex neural networks. PyTorch defines a computational graph as a Directed Acyclic Graph (DAG) where nodes represent operations (e.g., addition, multipl 4 min read Deep Learning with PyTorch | An Introduction PyTorch in a lot of ways behaves like the arrays we love from Numpy. These Numpy arrays, after all, are just tensors. PyTorch takes these tensors and makes it simple to move them to GPUs for the faster processing needed when training neural networks. It also provides a module that automatically calc 7 min read Like