Image Classification

Understanding 10+ Visual Transformer Models

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Understanding 10+ Visual Transformer Models

Transformers, as an attention-based encoder-decoder architecture, have not only revolutionized the field of Natural Language Processing (NLP) but have also made groundbreaking contributions in the field of Computer Vision (CV). Compared to Convolutional Neural Networks (CNNs), Visual Transformers (ViT) rely on their excellent modeling capabilities, achieving outstanding performance across multiple benchmarks such as ImageNet, COCO, … Read more

Introduction to MVTec Deep Learning Tool

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Introduction to MVTec Deep Learning Tool

Introduction to MVTec Deep Learning Tool The MVTec Deep Learning Tool V0.4.3 has added model training and evaluation features based on its image classification and object detection annotation capabilities. This feature helps customers train deep learning models and provides relevant evaluation information, including heatmaps of predicted categories and confusion matrices for inference images. In the … Read more

Extracting Winter Wheat Sowing Area in Henan Using Deep Learning

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Extracting Winter Wheat Sowing Area in Henan Using Deep Learning

Wheat is one of the most important staple foods in China, and the sowing area and yield of wheat are directly related to national food security and social stability. At the same time, the sowing area of wheat is an important link in monitoring wheat growth and estimating yield. With the development of remote sensing … Read more

Build Your First Image Classification Model in Just 10 Minutes!

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Build Your First Image Classification Model in Just 10 Minutes!

Author: Pulkit Sharma; Translator: Wang Weili; Proofreader: Ding Nanya This article is about 3400 words, recommended reading time is 10 minutes. This article introduces the process of building a deep learning model for image recognition, providing a basic framework for beginners to solve image recognition problems by stating the actual competition problem, introducing the model … Read more

Understanding the K-Nearest Neighbors Algorithm

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Understanding the K-Nearest Neighbors Algorithm

What is the K-Nearest Neighbors Algorithm (KNN)? The K-Nearest Neighbors algorithm (KNN) is a simple and intuitive machine learning algorithm widely used for classification and regression tasks. Its core idea is based on the principle of “birds of a feather flock together,” finding the K most similar neighbors by comparing the distance between a new … Read more

Understanding the KNN Algorithm Thoroughly

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Understanding the KNN Algorithm Thoroughly

Hello everyone, I am Xiaohan. Today, I will share a powerful algorithm model, KNN. KNN (K-Nearest Neighbors) is an instance-based supervised learning algorithm widely used in classification and regression tasks. Its core idea is: given a sample, calculate its distance to all samples in the training set, find the K nearest samples (the nearest neighbors), … Read more

Predicting House Prices Using KNN for Regression, Classification, and Outlier Detection

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Predicting House Prices Using KNN for Regression, Classification, and Outlier Detection

Full Article Link: https://tecdat.cn/?p=33917 KNN is a non-parametric learning algorithm, which means it makes no assumptions about the underlying data. This is a very useful feature because most client data does not really follow any theoretical assumptions, such as linear separability, uniform distribution, etc. (Click the “Read the Original” link at the end of the … Read more

Improving kNN Matching Effect for Dating Websites (With Source Code)

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Improving kNN Matching Effect for Dating Websites (With Source Code)

Improving kNN Matching Effect for Dating Websites This article follows up on the previous one, in which we briefly introduced the basic principles of the kNN algorithm. Using the context of dating website matching, we implemented data parsing from a text file using Python and created scatter plots with matplotlib for data analysis. In this … Read more

KNN Principles and Six Use Cases

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KNN Principles and Six Use Cases

Nearest Neighbor Algorithm Algorithm Principles The principle of the nearest neighbor method is to find a predefined number of training samples that are closest to the new point and predict the label from them. The number of samples can either be a user-defined constant (k-nearest neighbor learning) or can vary based on the local density … Read more

Implementing kNN Algorithm for Nearest Neighbor Classification

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Implementing kNN Algorithm for Nearest Neighbor Classification

“Birds of a feather flock together” is a common phenomenon in real life, indicating that similar things are likely to have similar attributes. Using this idea, machine learning can classify data, assigning it to the same category, such as similar or “nearest” neighbors. Today, let’s learn about nearest neighbor classification. 1. Understanding Nearest Neighbor Classification … Read more