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
By / Development Technology Promotion Engineer Khanh LeViet TensorFlow Lite (tensorflow.google.cn/lite) is the official framework for running TensorFlow model inference on edge devices. TensorFlow Lite is deployed on over 4 billion edge devices worldwide and supports IoT devices and microcontrollers based on Android, iOS, and Linux. Since the initial release of TensorFlow Lite at the … Read more
Translation / TF Community Translation Team Introduction: We previously introduced “The Future of Machine Learning – Miniaturization (Part 1)&(Part 2)“, and many friends expressed great interest and raised some questions. This article provides a detailed introduction to microcontrollers. We welcome friends who are interested and have needs in this area to communicate with us more~ … Read more
Yunzhong from Aofeisi Quantum Bit Edited | Public Account QbitAI In today’s era of artificial intelligence, most people pay attention to deep learning technologies, but please do not overlook the understanding of traditional machine learning techniques. In fact, when you truly engage in AI work, you will find that the dependence on traditional machine learning … Read more
Original by Machine Heart Authors: Yi Jin, Joni Chuang Participants: Panda Didrik Nielsen’s master’s thesis at the Norwegian University of Science and Technology, “Tree Boosting with XGBoost: Why Does XGBoost Win ‘Every’ Machine Learning Competition?” analyzes the differences between XGBoost and traditional MART and its advantages in machine learning competitions. The technical analysts at Machine … Read more