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TensorFlow
@TensorFlow
TensorFlow is a fast, flexible, and scalable open-source machine learning library for research and production.
Mountain View, CAtensorflow.orgJoined February 2011

TensorFlow’s posts

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👀 Looking for a community of devs? 💡 Share your projects, ask questions, and connect with fellow developers on the TensorFlow Forum! Join this thriving community for discussions, support, and collaboration ➡️ goo.gle/2TJHykJ
TensorFlow Forum
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🎉 The wait is over! TensorFlow 2.0 is finally here. Driven by community feedback, this release provides a complete set of tools for developers, enterprises, and researchers to easily build ML applications. Read the blog ↓
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⚡️ Accelerating TensorFlow 2 performance on Mac ’s new Mac-optimized TensorFlow 2.4 fork lets you speed up training on Macs, resulting in up to 7x faster performance on platforms with the new M1 chip! Learn how ↓
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Today we’re launching Seedbank, a place to discover interactive ML examples which you can run from your browser, no set-up required. Each example can be edited, extended, and adapted into your own project. Read 's post for more info ↓ bit.ly/2JhRG9E
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Happy 3rd birthday TensorFlow! We've come a long way since the first release in 2015 & TensorFlow wouldn't be the framework it is today without you. As we work on #TensorFlow20, look at all the features we've added over the years to make TensorFlow easier to use. #HappyBirthdayTF
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We have a recommendation for that Introducing TensorFlow Recommenders, an open-source package that makes building, evaluating, and serving recommender models easy. Find recommendations for movies, restaurants, and much more! Get started → goo.gle/2HrCwDq
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It’s official! 🙌 We’re excited to announce the TensorFlow Lite plugin for Flutter has been migrated to our GitHub account. The latest version includes new features and example apps, such as object detection through a live camera feed. Learn more → goo.gle/45uGW20
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Swift for TensorFlow is now open source! It is a new programming model for machine learning combining the performance of graphs and the expressivity of Eager execution, with first-class autodiff built right into the compiler Learn more on the TF blog ↓
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TensorFlow 2.2.0 has been released! Check out the release notes for major features and improvements such as a new Profiler for TF 2 for CPU/GPU/TPU, updates on tf.distribute, tf.keras, and more. Learn more here ↓
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We are excited to announce tfjs-vis, a browser based visualization library for TensorFlow.js. It has charts to let you see the data flowing through your model, and a convenient hide-able drawer to put them in while developing your apps. Check it out → bitly.com/2R6XeZu
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Graph models allow for all kinds of optimization, but it can be tricky to move between Python and graph code. To help with this, TensorFlow has AutoGraph, a new feature that converts Python code into pure TensorFlow graph code. Learn more on the blog ↓
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👀 Iris tracking is now supported in the browser In this blog, you'll learn how to incorporate Iris landmark tracking to your models through TensorFlowJS. This work is made possible by the MediaPipe Iris model. Learn how → goo.gle/2HUt0cH
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"I was unemployed due to COVID, and decided to take the TensorFlow Certificate exam. I'm now a Google employee, the certificate helped my resume stand out from other applications." Fran Marmousez, Conversational AI Engineer at Google. Learn more → goo.gle/2VBU5Yv
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TensorFlow 2.0.0-rc0 has been released! With a focus on simplicity and ease of use, this release features API simplification, easy model building with Keras, and more. See the full release notes for details on added features and changes ↓
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Introducing TensorFlow Probability: empowering ML researchers and practitioners to build sophisticated models quickly, leveraging state-of-the-art hardware Read about it on the TensorFlow blog ↓
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🎉 TensorFlow 2.4 is here! This latest release includes increased support for distributed training and mixed precision, new NumPy frontend, and tools for monitoring and diagnosing performance bottlenecks. Learn more ↓
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Good hyperparameters lead to ML success! 🙌 Learn how you can find the best parameters with Keras Tuner— a fully-featured, scalable, and easy-to-use hyperparameter tuning library for Keras and beyond. Read the blog → goo.gle/2uCnJzt
hyperparameters with keras tuner
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🦸‍♀️🦸‍♂️Superpowers straight from your browser Discover how to achieve superpowers in the browser by embracing machine learning in JavaScript using TensorFlow.js, and follow along as shows several creative prototypes. Check it out → goo.gle/37n7FkE
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🎉To make ML accessible beyond ML experts, we’ve released Simple ML for Sheets. #SimpleMLForSheets is an add-on for Google Sheets from the TensorFlow team that can help make machine learning accessible to all without programming or ML expertise. Try it→ goo.gle/sml
Owl mascot with text "Simple ML for Sheets"
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🆕 If you're new to quantum machine learning... Check out this post to see how TensorFlow Quantum (TFQ) helped one researcher publish their first paper, and what it can do for you! Learn more → goo.gle/33kURu2
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The release of the Beta for TensorFlow 2.0 is here! We've closed over 100 issues you reported against the alpha release. Your feedback has helped us get to where we are today, please keep it coming! Get more info here → goo.gle/2IpE5OZ
TensorFlow Beta Release
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🧘‍♂️ MoveNet is now on mobile! The state-of-the-art pose estimation model finally comes to mobile via TensorFlow Lite with CPU acceleration enabled. Now you can run pose estimation in realtime on modern smartphones. Learn more 👉 goo.gle/3m494pn
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TensorFlow Serving has released version 1.8.0 & now supports a RESTful API out of the box! You can now easily make classification, regression & prediction requests to your TensorFlow models using JSON objects. Learn more here ↓ bit.ly/2y2xR5N
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⚡️TensorFlow 2.1.0 is out NOW!⚡️ This will be the last TF release supporting Python 2. Please see the full release notes for details on added features and changes! Learn more here ↓
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☁️ Train your model with TensorFlow Cloud The TensorFlow Cloud repository provides APIs that allow you to go from debugging and training your Keras models locally to distributed training in the cloud with no code changes to your model. Learn more → goo.gle/3fw72Yg
TensorFlow Cloud code snippet
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