Source: Basics and Advanced of Deep Learning This article is approximately 2800 words long and is suggested to be read in 9 minutes. This article visually demonstrates the prediction process of the XGBoost machine learning model to help you better understand it. The algorithm using XGBoost often achieves good results in Kaggle and other data … Read more
Introduction Since the beginning of the 21st century, with the rapid development of the social economy, the demand for resources has continued to increase. However, shallow mineral resources are increasingly depleted, forcing mining work to shift underground. After blasting and excavating deep tunnels, the surrounding rock inevitably produces a loose circle due to the coupling … Read more
Why are tree-based machine learning methods, such as XGBoost and random forests, superior to deep learning on tabular data? This article provides reasons behind this phenomenon, selecting 45 open datasets and defining a new benchmark to compare tree-based models with deep models, summarizing three reasons to explain this phenomenon. Deep learning has made significant progress … Read more
This article will explain in detail the introduction, functionality, and value ranges of the ten most commonly used hyperparameters in XGBoost, and how to use Optuna for hyperparameter tuning. For XGBoost, the default hyperparameters work fine, but if you want to achieve the best performance, you need to adjust some hyperparameters to match your data. … Read more
Boosting algorithms are a class of machine learning algorithms that build a strong classifier by iteratively training a series of weak classifiers (usually decision trees). In each round of iteration, the new classifier is designed to correct the errors of the previous classifier, thus gradually improving the overall classification performance. Despite the rise and popularity … Read more
Abstract: In recent years, cognitive load overload has become an important factor affecting the effectiveness of online learning. To address this issue, the article focuses on the assessment of cognitive load in online learning, first designing a research framework for online learning cognitive load assessment based on multimodal data, which includes three parts: multimodal data … Read more
Source｜Public Account: Heart of Autonomous Driving Autonomous vehicles require continuous environmental perception to understand the distribution of obstacles for safe driving. Specifically, 3D object detection is a crucial functional module as it can predict the category, location, and size of surrounding objects simultaneously. Generally, autonomous cars are equipped with multiple sensors, including cameras and LiDAR. … Read more
1. Introduction In today’s field of artificial intelligence, the application of multimodal large models in robotics is becoming increasingly widespread. This article aims to introduce how to convert multimodal large models to the gguf format and quantize them for efficient deployment on the ollama platform. Through this process, we achieve more efficient model operation and … Read more
0 Introduction Software design patterns are an important aspect of software engineering, helping developers solve common design problems and improve the quality and maintainability of software systems.[1] Traditional teaching methods for software design patterns courses have some issues, such as cumbersome preparation of teaching cases by teachers and the difficulty for students to understand and … Read more
You can “listen” to this article anytime on your mobile phone or computer Edge browser. Key Points: Generative AI, based on predictive models, can accurately perceive numbers, possess absolute mathematical knowledge and undeniable logic, and tirelessly reason to derive the best or optimal output based on current prompts. The tight connection between logic and the … Read more