How TensorFlow Works? | Tensorflow Architecture | #tensorflow

How TensorFlow works internally is the question that many ML and AI developers ask very often. In this video, Detailed TensorFlow Architecture is explained step by step. The video gives introduction of TensorFlow, talks about different aspects of Implementation of TensorFlow project, Extensions in TensorFlow that makes it more flexible, How TensorFlow optimizes the execution and the future scopes on which TensorFlow team is working on. TensorFlow is an open-source end-to-end platform for Machine Learning. It provides a comprehensive ecosystem of tools for developers, enterprises, and researchers who want to push the state of the art of Machine Learning and build scalable ML powered applications. Make sure to subscribe to our YouTube channel to stay up to date! TensorFlow Research: https://www.tensorflow.org/about/bib Background Music By AHOMI: https://tunetank.com/t/1voc/3476-new-day/ Follow me on below social media, Discord: https://discord.gg/GkcbM5bwZr LinkedIn: https://www.linkedin.com/in/ashutosh-hathidara-88710b138/ Github: https://github.com/ashutosh1919 Behance: https://www.behance.net/devsense19 Dribble: https://dribbble.com/ashutosh1919 Facebook: https://www.facebook.com/laymanbrother.19/ Instagram: https://www.instagram.com/developer_sense/ YouTube : https://www.youtube.com/c/DevSense19 Patreon: https://www.patreon.com/devsense To know more about me, Checkout my website https://ashutoshhathidara.com/ Drop me mail if you need any help, [email protected] Tags: how tensorflow works,tensorflow tutorial,how tensorflow graph works,tensorflow architecture,deep learning,neural network,google developers,why tensorflow,why use tensorflow,tensorflow,tensorflow overview,what is tensorflow,introduction to tensorflow,tensorflow developers,machine learning,coding tensorflow,inside tensorflow,natural language processing zero to hero,natural language processing,ask tensorflow,tensorflow meets,google,laurence moroney,developers,developer,program,code Timestamps 00:00 Introduction 06:02 Implementation 11:11 Extensions For Flexibility 17:37 Optimizations 20:58 Parallelism 22:10 Future Work