February 19, 2020 — A guest article by Harshita Diddee, Divyanshu Sharma, Shivani Jindal and Shivam Grover

February 15, 2020 — Posted by Rina Panigrahy

February 12, 2020 — Posted by Marina Munkhoeva, PhD student at Skolkovo Institute of Science and Technology and AI Resident at Alphabet's X, Chase Roberts, Research Engineer at Alphabet's X, and Stefan Leichenauer, Research Scientist at Alphabet's X

February 10, 2020 — Posted by Mahdi Milani Fard, Software Engineer, Google Research

Most ML practitioners have encountered the typical scenario where the training data looks very different from the run-time queries on which the model is evaluated. As a result, flexible ML solutions such as DNNs or forests that rely solely on the training dataset often act unexpectedly and even wildly in parts of the input space not …

February 07, 2020 — Posted by Josh Gordon for the TensorFlow team

February 04, 2020 — Posted by Yannick Assogba, Software Engineer, Google Research, Brain team

We are pleased to announce that TensorFlow.js for React Native is now available for general use. We would like to thank everyone who gave us feedback, bug reports, and contributions during the alpha release and invite the broader community of React Native developers to try it out!

February 03, 2020 — When the TensorFlow YouTube channel launched in 2018, we had a vision to inform and inspire developers around the world about what was possible with Machine Learning. With series like Coding TensorFlow showing how you can use it, and Made with TensorFlow showing inspirational stories about what people have done with TensorFlow and much more, the channel has grown greatly. But we learned an import…

January 30, 2020 — Posted by Lucia Li, TensorFlow Lite Intern

January 29, 2020 — Posted by Tom O’Malley

The success of a machine learning project is often crucially dependent on the choice of good hyperparameters. As machine learning continues to mature as a field, relying on trial and error to find good values for these parameters (also known as “grad student descent”) simply doesn’t scale. In fact, many of today’s state-of-the-art results, such as EfficientNet, were discove…

January 23, 2020 — Posted by the TensorFlow team

January 22, 2020 — Posted by Ruoyu Liu and Robert Crowe on behalf of the TFX team

TensorFlow Extended (TFX) is a platform for creating production-ready machine learning (ML) pipelines. TFX was created by Google and provides the backbone of Google’s ML services and applications, and now Google has open sourced TFX for anyone who wants to create production ML pipelines.

January 16, 2020 — A guest post by Tencent QQ Music Audio Engineering Team

January 15, 2020 — A guest post by Michael Wang and Noemie Guerin from Companion

January 14, 2020 — Posted by Kangyi Zhang, Sandeep Gupta, and Brijesh Krishnaswami

January 07, 2020 — A guest post by Liang Bao, Chengqi Lv, from Ecovacs Robotics AI Department.
Translated by Ziqian Xu, from Ecovacs Robotics Technology Development Department.

December 23, 2019 — Posted by the TensorFlow Team

2019 was an exciting year for TensorFlow. From releasing TensorFlow 2.0 and several product updates to hosting a global roadshow in 11 different countries and the first ever TensorFlow World, this year highlighted how TensorFlow is helping to empower developers, researchers, and enterprises around the world to solve challenging, real-world problems with ML. With Tens…

December 19, 2019 — By Jordan Grimstad
It’s been a year and a half since we introduced TensorFlow Hub, an open-source repository of ready to use pre-trained models published by Google and DeepMind. Since then, we’ve published hundreds of models -- some that are general-purpose and fine-tunable to specific tasks, others which are more specialized -- to help you get faster, smarter ML applications even with little dat…

December 16, 2019 — Posted by Karim Nosseir and Sachin Joglekar, Software Engineers

Digital Signal Processors (DSPs), such as Hexagon DSPs, are microprocessors present on most modern phones alongside other compute units such as the CPU and GPU. Developed with the primary goal of improving communication and multimedia processing, these chips can dramatically speed up model inference on mobile / edge devices. DSPs are …

December 12, 2019 — Posted by Pavel Senchanka, Software Engineering Intern at Google

TensorFlow Lite is an industry-leading solution for on-device inference with machine learning models. While a complete training solution for TensorFlow Lite is still in progress, we're delighted to share with you a new on-device transfer learning example. This illustrates a way of personalizing your machine learning models on-d…

December 11, 2019 — Posted by Catherina Xu and Tulsee Doshi, Product Managers, Google Research

While industry and academia continue to explore the benefits of using machine learning (ML) to make better products and tackle important problems, algorithms and the datasets on which they are trained also have the ability to reflect or reinforce unfair biases. For example, consistently flagging non-toxic text comments fro…