Deep learning has become particularly popular in recent years, much like big data did five years ago. However, deep learning primarily falls within the field of machine learning. In this article, we will discuss the differences in algorithm processes between machine learning and deep learning. The Algorithm Processes of Machine Learning and Deep Learning I … Read more
Introduction: Research on model interpretability has become a hot topic at research conferences in the past two years, as people are no longer satisfied with just the performance of the model but are also pondering the reasons behind the model’s performance. This kind of thinking helps optimize models and features, enhances understanding of the models … Read more
Machine Learning and Deep Learning Algorithm Processes Finally got into the AI research program, but I don’t know what the difference is between machine learning and deep learning. It feels like everything is deep learning. Wow, I heard that a senior has been tuning parameters for 10 months to prepare for the T9 model with … Read more
Source | Zhihu: Deep Blue Academy Artificial intelligence is becoming increasingly popular today, with many terms constantly surrounding us: artificial intelligence, machine learning, deep learning, etc. Many people have a vague understanding of the meanings of these high-frequency terms and their relationships. To help everyone better understand artificial intelligence, this article explains these terms in … Read more
In the past few years, based on my work experience and communication with other data scientists, including content read online, I have compiled what I consider to be the most important machine learning algorithms. This year, I want to provide more models across categories based on last year’s article. I hope to offer a treasure … Read more
◎ Tech Daily Reporter Zhang Mengran Can machines learn, but can they forget? A friend said, “If you unplug the power without saving, won’t it forget!” The issue is not that simple. Learning to Forget: A Major Challenge for AI The purpose of machine learning is to use computers as tools to simulate human learning … Read more
Introduction For almost all machine learning algorithms, whether supervised learning, unsupervised learning, or reinforcement learning, they ultimately boil down to solving optimization problems. Therefore, optimization methods play a central role in the derivation and implementation of machine learning algorithms. In this article, I will provide a comprehensive summary of the optimization algorithms used in machine … Read more
Source: Zhihu Abner says AI This article is approximately 4500 words long and suggests a reading time of 9 minutes. This article summarizes the pros and cons of the top 10 machine learning algorithms. 1. Logistic Regression The binary logistic regression model is a classification model represented by the conditional probability distributionP(Y|X), which takes the … Read more
Machine learning is a hot topic, but apart from the teachers who know it well, very few people can explain what it’s all about. If you read articles about machine learning online, you’re likely to encounter one of two situations: either a heavy academic trilogy filled with various theorems (I struggle to grasp even half … Read more
Introduction For almost all machine learning algorithms, whether supervised learning, unsupervised learning, or reinforcement learning, the final goal generally boils down to solving an optimization problem. Therefore, optimization methods occupy a central position in the derivation and implementation of machine learning algorithms. In this article, the author will provide a comprehensive summary of the optimization … Read more