Reference Information (Click Title to Read Full Text) Huang Dongmei, Wang Yifan, Hu Anduo, et al. Detection method of false data injection attack based on unsupervised and supervised learning[J]. Electric Power Engineering Technology, 2024, 43(2):134-141. HUANG Dongmei, WANG Yifan, HU Anduo, et al. Detection method of false data injection attack based on unsupervised and supervised … Read more
Selected from StatsBot Author: Anton Karazeev Translated by Machine Heart Contributors: Qianshu, Huang Xiaotian This article is reproduced with permission from “Machine Heart” Reproduction prohibited Generative Adversarial Networks (GANs) are a class of neural networks used in unsupervised learning, which help to solve tasks such as generating images from text, improving image resolution, drug matching, … Read more
Source: Turing Artificial Intelligence Depending on the type of data, modeling a problem can be approached in different ways. In the field of machine learning or artificial intelligence, people first consider the learning method of the algorithm. There are several main learning methods in machine learning. Categorizing algorithms according to their learning methods is a … Read more
The learning of artificial neural networks mainly refers to the use of learning algorithms to adjust the connection weights between neurons, so that the network output better matches reality. Learning algorithms are divided into supervised learning and unsupervised learning. Supervised learning, also known as teacher-based learning, is a learning method where a set of training … Read more
Source ： Turing Artificial Intelligence According to the different types of data, modeling a problem can be done in various ways. In the field of machine learning or artificial intelligence, the learning method of the algorithm is usually the first consideration. There are several main learning methods in machine learning. Classifying algorithms by learning methods … Read more
Author: Zhang Jianzhong Source: http://blog.csdn.net/zouxy09/article/details/8775518 Deep learning is a new field in the study of machine learning, motivated by the establishment and simulation of neural networks that analyze and learn like the human brain. It mimics the mechanisms of the human brain to interpret data such as images, sounds, and text. Deep learning is a … Read more
Selected from arXiv Authors: Guillaume Lample et al. Translation by Machine Heart Contributors: Zhang Qian, Lu Recently, researchers from FAIR proposed two variants of machine translation models, one being a neural model and the other based on phrases. The researchers combined two recently proposed unsupervised methods, simplifying the structure and loss functions, resulting in a … Read more
Selected from KDnuggets Translated by Machine Heart Author:Michael Dietz Contributors: Jane W, Yan Qi, Wu Pan Generative Adversarial Networks (GANs) have gained significant attention in the research community recently. In this article, Michael Dietz, founder of Waya.ai, explains why GANs hold such potential and illustrates how GANs work through a vivid comparison with boxing matches. … Read more
This article will explain the essence of machine learning algorithms and the principles of machine learning algorithms, as well as their applications in three aspects, helping you understand machine learning algorithmsMachine Learning Algorithms。 Machine Learning AlgorithmsMachine Learning Algorithms 1. Essence of Machine Learning AlgorithmsDefinition of Machine Learning Algorithms: Machine Learning Algorithms are defined as: mathematical … Read more
Author: Sebastian Ruder, Translated by QbitAI 2018 was a significant year for the field of NLP. The most notable was BERT, which swept various NLP tests and was hailed as the beginning of a new era in NLP. But in 2018, there was more than just BERT. Recently, Irish NLP researcher Sebastian Ruder wrote an … Read more