What is PyTorch | PyTorch For Beginners | PyTorch Explained in 8 Minutes | Intellipaat

[Music] py toch is a powerful python library that lets you build and train deep learning models like neural networks in terms of popularity it exceeds 880,000 stars on GitHub by the end of 2023 and continued to be a widely discussed topics in kaggle forums highlights its active community and Vibrant Community presence hello and welcome to this video on what is py toch by intellipad in this particular video we will explore what is py to and look into into its popularity but before we do that make sure you enable both the Subscribe button and Bell icon for intellipath YouTube channel so that you won't miss any updates coming from our end so guys py toch is basically an open-source machine learning library which is used for developing and training neural networks based on deep learning models and also pyto can be used with python as well as C++ it is primarily developed by Facebook's AI research group and later it got backed by other big giants like Microsoft Salesforce and Uber which made it immensely popular in research labs and unlike most of other deep learning Frameworks like tensor flow which uses static computation graphs py toch uses Dynamic computation which allows greater flexibility in building complex architectures py toch uses Core Concepts like classes structures and conditional Loops that are a lot familiar to our eyes and this makes it a lot simpler than other Frameworks like tensor flow that bring in their own programming Styles third pyo is fevered over other deep learning Frameworks like tensorflow and Kos since it uses Dynamic computation graph and is completely pythonic so it allows scientists developers and neural network debuggers to run and test portions of the code in real time now if we talk about how py to actually works then there comes the two main features that tells the working of py toch that is tensors and dynamic computation graphs so py to basically Works through a fascinating interplay of dynamic computation graphs and tensors now if we talk about tensors they are the data workhorses so just think of tensors as a supercharged array that can hold numers vectors matrices or even higher Dimension data and they effectively represent the diverse data types used in deep learning second pyo seamlessly utilizes the parallel processing power of gpus and CPUs to perform fast calculations on tensors and significantly speeding up the computations third Point pytorch offers a rich collection of functions for various mathematical and statistical operations on tens which enables you to manipulate and transform data as it is needed now if we talk about Dynamic computation graphs py to constructs the computation graph dynamically as you execute your code and offers several advantages like rapid experimentation and intuitive debugging which means tracing errors back through the graph to pinpoint issues and understand Model Behavior more effectively second this graph usually depicts how your model processes data where node represents operations like addition matrix multiplication active functions and edges represents the flow of tensors between those nodes third point is pytorch optimizes graph execution for performance and quickly test different architectures or ideas so tensors and dynamic computation graphs together form the backbone of pyo's flexibility and power and they enable you to work work with complex data structures efficiently build adaptable model architectures and train deep learning models effectively now let's look into the features of pych so these are some of the main features of py now let's look it one by one so first one is production ready so py to provides an ease of use and flexibility in eager mode while seamlessly transitioning to graph mode for speed optimization and functionality in C++ runtime environments second is distributed training which says optimize performance in both research and production by taking advantage of native support for asynchronous execution of collective operations and peer-to-peer communication that is accessible from Python and C++ third one is toor so Tor Ser is an ease to use tool for deploying py torch models at scale and it supports features like multimodel serving logging matrices and the creation of restful end points for application integration coming to fourth one it is robust ecosystem so it has an active community of researchers and developers that have built a rich ecosystem of tools and libraries for extending py toch fifth is its Mobile support So py to supports an endtoend workflow from python to deploy re M on IOS and Android and sixth one is native onnx support so it exports modules in a standard on nnx or open neural network exchange format for direct access to onnx compatible platforms run times visualizers and more now if we look into some of the benefits first there is a large and Vibrant Community at py torch.com org Community with excellent documentations and tutorials second is pyo is written in Python and integrated with some of the popular python libraries like numai ccii and syon third one is it is well supported by Major Cloud platforms fourth pyo supports CPU GPU and parallel processing as well as distributed training now if we talk about the applications of pyo then computer vision natural language processing and generative modeling are some of the main applications of py do and if we talk about computer vision then image classification object detection image segmentation and video analysis comes in in natural language processing we have machine translation sentiment analysis text summarization and Chan boards and in generative modeling image generation text generation and music generation comes in so that's all we have for this video if you liked it please give it a thumbs up and do share it with your fellow mates also don't forget to subscribe to our YouTube channel if you haven't done it till now thank you so much just a quick info guys intellipath offers machine 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Id: 9BYrnnyEveA

Length: 8min 11sec (491 seconds)

Published: Mon Jan 15 2024