Keras vs TensorFlow vs PyTorch

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A Data Science Enthusiast with in-hand skills in programming languages such as Java Python. Bookmark Become a Certified Professional Keras, TensorFlow and PyTorch are among the top three frameworks that are preferred by Data Scientists as well as beginners in the field of Deep Learning.This comparison onKeras vs TensorFlow vs PyTorchwill provide you with a crisp knowledge about the top Deep Learning Frameworks and help you find out which one is suitable for you. In this blog you will get a complete insight into the above three frameworks in the following sequence:Introduction to Keras, TensorFlow PyTorchComparison Factor sFinal VerdictIntroductionKerasKeras is an open source neural network library written in Python. It is capable of running on top of TensorFlow. It is designed to enable fast experimentation with deep neural networks.TensorFlowTensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library that is used for machine learning applications like neural networks.PyTorchPyTorch is an open source machine learning library for Python, based on Torch. It is used for applications such as natural language processing and was developed by Facebooks AI research group.Keras vs Tensorflow vs PyTorch | EdurekaThis Edureka video on Keras vs TensorFlow vs PyTorch will provide you with a crisp comparison among the top three deep learning frameworks. It provides a detailed and comprehensive knowledge about Keras, TensorFlow and PyTorch and which one to use for what purposes.Comparison FactorsAll the three frameworks are related to each other and also have certain basic differences that distinguishes them from one another. So lets have a look at the parameters that distinguish them:Level of APISpeedArchitectureDebuggingDatasetPopularityLevel of APIKeras is a high-level API capable of running on top of TensorFlow, CNTK and Theano. It has gained favor for its ease of use and syntactic simplicity, facilitating fast development.TensorFlow is a framework that provides both high and low level APIs. Pytorch, on the other hand, is a lower-level API focused on direct work with array expressions. It has gained immense interest in the last year, becoming a preferred solution for academic research, and applications of deep learning requiring optimizing custom expressions.SpeedThe performance is comparatively slower in Keras whereas Tensorflow and PyTorch provide a similar pace which is fast and suitable for high performance.ArchitectureKeras has a simple architecture. It is more readable and concise . Tensorflow on the other hand is no t very easy to use even though it provides Keras as a framework that makes work easier. PyTorch has a complex architecture and the readability is less when compared to Keras.DebuggingIn keras, there is usually very less frequent need to debug simple networks. But in case of Tensorflow, it is quite difficult to perform debugging. Pytorch on the other hand has better debugging capabilities as compared to the other two.DatasetKeras is usually used for small datasets as it is comparitively slower. On the other hand, TensorFlow and PyTorch are used for high performance models and large datasets that require fast execution.PopularityWith the increasing demand in the field of Data Science, there has been an enormousgrowth of Deep learning technology in the industry. With this, all the three frameworks have gained quite a lot of popularity. Keras tops the list followed by TensorFlow and PyTorch. It has gained immense popularity due to its simplicity when compared to the other two. Get in-de pth Knowledge of Deep Learning Enroll Now These were the parameters that distinguish all the three frameworks but there is no absolute answer to which one is better. The choice ultimately comes down to Technical backgroundRequirements andEase of UseFinal VerdictNow coming to the final verdict of Keras vs TensorFlow vs PyTorch lets have a look at the situations that are mostpreferable for each one of these three deep learning frameworksKeras is most suitable for:Rapid PrototypingSmall DatasetMultiple back-end supportTensorFlow is most suitable for:Large DatasetHigh PerformanceFunctionalityObject DetectionPyTorch is most suitable for:FlexibilityShort Training DurationDebugging capabilitiesNow with this, we come to an end of this comparison onKeras vs TensorFlow vs PyTorch. I Hope you guys enjoyed this article and understood which Deep Learning Framework is most suitable for you.Now that you have understood the comparison between Keras, TensorFlow and PyTorch,check out theAI and Deep Learning With TensorflowbyEdureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe. This Certification Training is curated by industry professionals as per the industry requirements demands. You will master concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras TFLearn. Got a question for us? Please mention it in the comments section of Keras vs TensorFlow vs PyTorch and we will get back to you.Recommended blogs for you Apache Cassandra Advantages Read Article How to Implement Call by Reference in C++ Read Article Load Testing using JMeter : How to Measure Performance in CMD Read Article JMeter Correlation: The Best Way for Extracting Variables Read Article CSM vs CSPO: Which Is The Right Agile Certification For You? Read Article Golang Tutorial Learn Golang by Examples Read Article What is Software Quality Assurance Testing and How does it Work? Read Article How To Write A C Program For Deletion And Insertion? 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