↑↑↑ Follow and “Star” Datawhale Daily Insights & Monthly Learning Teams, Don’t Miss Out Datawhale Insights Author: Li Zuxian, Shenzhen University, Datawhale University Group Member Zhihu Address: http://www.zhihu.com/people/meng-di-76-92 Today, I will mainly introduce XGBoost, one of the three giants in machine learning ensemble methods. This algorithm has previously shone in machine learning competitions and is … Read more
Source: Machine Learning Algorithms This article is about 8400 words long and is recommended for a 10-minute read. This article provides a detailed explanation of the engineering application methods of XGBoost. The illustrated machine learning practical application demonstrates the application process and chain of machine learning algorithms in a case-driven and code-driven manner, mastering the … Read more
Today I will share the complete implementation of local inference deployment for multimodal models. To facilitate everyone’s understanding of the entire process, I have organized the steps and provided detailed results. Friends who are interested should try it out quickly. 1. Introduction to DeepSeek-R1 andllama3.2-vision Models DeepSeek R1 is an open-source inference-optimized large language model … Read more
First, you need to install Python and find a user-friendly compiler, like RStudio. Next, you need to find the data. The original author has placed the data on Kaggle (https://www.kaggle.com/datasets/jessicali9530/lfw-dataset/code), but I have already downloaded it. Just reply with “Face Recognition” to get the complete data. Be sure to set the reading path to access … Read more
Introduction to CNN (Convolutional Neural Network) A convolutional neural network is a deep learning algorithm specifically designed for processing images and videos. It takes images as input, extracts and learns features from the images, and classifies them based on the learned features. The inspiration for this algorithm comes from a part of the human brain, … 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
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
In this article, based on the stock price time series data of 20 companies, we will look at four different ways to classify these companies according to the correlation between their stock prices. Apple (AAPL), Amazon (AMZN), Facebook (META), Tesla (TSLA), Alphabet (Google) (GOOGL), Shell (SHEL), Suncor Energy (SU), ExxonMobil (XOM), Lululemon (LULU), Walmart (WMT), … Read more
Click on "Xiaobai Learns Vision" above, select "Star" or "Pin" Heavy content delivered immediately Editor’s Recommendation A simple summary is whether it is supervised (supervised) or not, which depends on whether the input data has labels (label). If the input data has labels, it is supervised learning; if there are no labels, it is unsupervised … Read more
Click on 'Xiao Bai Learns Vision' above, select 'Star' or 'Top' 1. Model Iteration Methods In practical applications of machine learning models, the model is usually iterated based on newly added data. Common model iteration methods include the following: 1. Retrain a model using the full dataset by directly merging historical training data with the … Read more