Written by / Li Xihan, Google Developers Expert This article is excerpted from “Simple and Rough TensorFlow 2.0” In the previous article, we introduced the widely used convolutional neural networks in the field of images and their implementation in TensorFlow 2.0. This article continues to introduce another widely popular neural network architecture, namely the recurrent … Read more
By / Google TensorFlow Team TensorFlow has evolved into one of the most popular and widely adopted machine learning platforms in the world, and we sincerely thank all the developers and their contributions who have supported us along the way: Researchers: (Predicting earthquake aftershocks, detecting prostate cancer …… ) Developers: (Identifying diseased plants, applications to … Read more
Written by / TensorFlow Team Welcome to the second part of the introduction to TensorFlow Datasets and Estimators series (click here for the first part). In this article, we will introduce Feature Columns – a data structure that describes the features needed for the estimator to train and make predictions. As you will see below, … Read more
Written by Laurence Moroney, AI Technology Advocate We know that machine learning technology can be used to solve various scenarios (such as healthcare) important tasks, and educators and experts in various fields are guiding and training developers on how to use machine learning technology to solve real-world problems. We are excited to share with everyone … Read more
Written by / Pankaj Kanwar and Fred Alcober With TensorFlow 2, developers, engineers, and researchers can achieve top-notch training performance across platforms, devices, and hardware, enabling them to work on their preferred platforms. Now, TensorFlow users can accelerate training speeds on Macs equipped with Apple’s new M1 chip or Intel chip using the optimized TensorFlow … Read more
AliMei Guide: As the main product of Alibaba in the field of idle circulation, Xianyu mainly develops mobile applications to solve the problem of re-circulation of idle goods/assets/time in personal domains, utilizing cross-end technologies (Base Flutter/Weex/Dart technology stack) and computer vision technologies (Base Tensorflow Lite) in cutting-edge practices on mobile terminals. This article is produced … Read more
By Hsin-Yuan Huang, Google/California Institute of Technology; Michael Broughton, Google; Jarrod R. McClean, Google; Masoud Mohseni, Google Machine learning is a data-driven science. Both big data research and industrial machine learning rely on large and high-quality data sources, and more data is often better. Machine learning algorithms, which depend on training data, exhibit superior performance … Read more
Author / Hangzhou Danna Technology Co., Ltd. Ai Homework is an APP that automatically grades math homework using AI technology. In just one year since its launch, it has accumulated over ten million users, helping many teachers and parents save time and improve efficiency. Within the Ai Homework APP, there is a feature called “Mental … Read more
By / Goldie Gadde and Nikita Namjoshi, TensorFlow TensorFlow 2.4 has been officially released! With increased support for distributed training and mixed precision, along with the introduction of a new NumPy frontend and tools for monitoring and diagnosing performance bottlenecks, this version highlights new features and enhancements in performance and scalability. New Features of tf.distribute … Read more
Written by Pavel Senchanka, Google Software Engineering Intern TensorFlow Lite is a leading solution for on-device inference of machine learning models. While a complete TensorFlow Lite training solution is still under development, we are eager to share a new example of on-device transfer learning. This article will introduce you to a practical approach for personalizing … Read more