Source | Machine Heart Editor | Jishi Platform Jishi Introduction This paper is the first comprehensive research introducing the progress of the SAM base model, focusing on its applications in various tasks and data types, discussing its historical development, recent advancements, and the profound impact on widespread applications. Artificial Intelligence (AI) is evolving towards AGI, … Read more
🫱Click here to join the group chat of 18 sub-fields (🔥Highly recommended)🫲 Paper Title: Depth Anything V2 Authors: Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao Project Address: https://depth-anything-v2.github.io/ Compiled by: xlh Reviewed by: Los Abstract: In monocular depth estimation research, the widely used labeled real images have many … Read more
Click the “blue text” above to follow us! Abstract Abstract: Architectural renderings can realistically simulate buildings and their effects after design completion, making them a necessary part of design expression in architectural education and a key component of architectural space visualization. However, traditional rendering methods are not only complex in software settings but also require … Read more
Without high-quality data, there are no high-quality data mining results. Missing data values are one of the common issues encountered in data analysis. When the proportion of missing data is very small, missing records can be directly discarded or handled manually. However, in actual data, missing data often accounts for a significant proportion. In this … Read more
One of the most common issues I encounter in data cleaning and exploratory analysis is handling missing data. First, we need to understand that there is no perfect method to solve this problem. Different issues have different data imputation methods—time series analysis, machine learning, regression models, etc., making it difficult to provide a universal solution. … Read more
When constructing machine learning models, selecting the optimal model is an inevitable challenge. If you can correctly understand the meanings of training sets, validation sets, and test sets, as well as how to partition the dataset, you will have a macro understanding of how to build machine learning models and gain confidence in the optimal … Read more
This article collects and organizes some common anomaly detection methods available on the public internet (with sources and code). Any shortcomings are welcome to be criticized and corrected. 1. Distribution-Based Methods 1. 3sigma Based on the normal distribution, the 3sigma criterion considers data points exceeding 3 sigma to be outliers. Figure 1: 3sigma def three_sigma(s): … Read more
Click on the above“Beginner Learning Vision” and choose to add Star or Pin. Important content delivered in real-time. Introduction For almost all machine learning algorithms, whether supervised learning, unsupervised learning, or reinforcement learning, it generally boils down to solving an optimization problem. Therefore, optimization methods occupy a central position in the derivation and implementation of … Read more
Click the "Xiaobai Learns Vision" above, select to add "Star" or "Top" Heavy content delivered to you first Summary of Model Explainability Introduction Currently, many machine learning models can make very good predictions, but they do not explain how they make these predictions well. Many data scientists find it difficult to understand why an algorithm … Read more
In the world of machine learning, accuracy is crucial. You strive to make your model more accurate by tuning and optimizing parameters, but you can never achieve 100% accuracy. This is the harsh reality of your prediction/classification models; they can never be zero-error. In this article, I will discuss why this happens and other methods … Read more