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
This document is aimed at users who need to provide backward compatibility for different versions of TensorFlow (whether code or data) and developers who wish to change TensorFlow while maintaining compatibility. Semantic Versioning 2.0 The public API of TensorFlow follows Semantic Versioning 2.0 (semver). Each TensorFlow version number is in the format MAJOR.MINOR.PATCH. For example, … 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
Academic Research Analyzing Urbanization Strategy Mechanisms of Crowd Behavior: A Case Study in Suzhou Exploring Urbanization Strategies by Dissecting Aggregate Crowd Behaviors: A Case Study in China Abstract Town development is an important stage of urbanization, and it has received increasing attention in sustainable economic growth strategies. Vitality, especially the vitality that measures crowd aggregation … 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
Hello, I am Cos Dazhuang!~ Recently, there has been a discussion in the community about the mixed classification tasks using XGBoost and KNN. Today, let’s take this opportunity to talk about the details and points to note, as well as the advantages they each bring~ As usual: If you think the recent articles are good! … Read more
This tutorial showcases the application process and workflow of machine learning algorithms through case studies and code-driven examples, enabling you to master the ability to build scenario modeling solutions and perform effect optimization.This article provides a detailed explanation of the engineering application methods of XGBoost. XGBoost is a powerful boosting algorithm toolkit that is the … Read more
Selected from Medium Translated by Machine Heart Contributors: Liu Tianci, Huang Xiaotian Despite the resurgence and popularity of neural networks in recent years, boosting algorithms still have indispensable advantages in scenarios with limited training sample sizes, shorter training times, and lack of tuning knowledge. This article compares three representative boosting algorithms: CatBoost, LightGBM, and XGBoost, … Read more
Dream Morning Reporting from Aofeisi Quantum Bit | Public Account QbitAI What algorithm is most likely to win prizes in machine learning competitions on Kaggle? You might say: Of course, it’s deep learning. Not really. According to statistics, the most winning algorithms are gradient boosting algorithms like XGBoost. This is strange. Deep learning shines in … Read more