XGBoost is an effective implementation for gradient classification and regression problems.It is fast and efficient, performing excellently in various predictive modeling tasks and is widely favored among winners of data science competitions (e.g., Kaggle winners), even if it is not the best.XGBoost can also be used for time series forecasting, although it requires converting the … 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
Machine learning can be divided into two main categories based on model types: supervised learning models and unsupervised learning models. 1. Supervised Learning Supervised learning typically uses training data with expert-labeled tags to learn a function mapping from input variable X to output variable Y. Y = f(X), where the training data is usually in … Read more
Madio.net Mathematics China /// Editor: Yu Dizongxuan This article is reprinted from the Mathematics Algorithm Club The distinction between statistics and machine learning has always been vague.Both in industry and academia, it has been widely believed that machine learning is just a shiny facade over statistics.Moreover, artificial intelligence, supported by machine learning, is also referred … Read more
Introduction Tesla’s FSD has popularized self-supervised learning, and large models like GPT also utilize the concept of self-supervised learning. As we know, the cost of supervised learning is prohibitively high, especially for complex tasks, such as FSD systems. Tesla has collected training data exceeding 400 million kilometers, and without the help of an “automated labeling … Read more
Source: Multimodal Machine Learning and Large Models This article is approximately 1800 words long and is recommended for a 10-minute read. This article introduces the evaluation of DeCUR in three common multimodal scenarios (Radar Optical, RGB Elevation, and RGB Depth) and demonstrates its continuous improvement, regardless of architecture, as well as in multimodal and modality-missing … Read more
Title: SPDET: Edge-Aware Self-Supervised Panoramic Depth Estimation Transformer With Spherical Geometry Edge-Aware Self-Supervised Panoramic Depth Estimation Transformer With Spherical Geometry Authors: Chuanqing Zhuang; Zhengda Lu; Yiqun Wang; Jun Xiao; Ying Wang Source Code Link: https://github.com/zcq15/SPDET Abstract Panoramic depth estimation has become a hot topic in 3D reconstruction technology because it provides an omnidirectional spatial field … Read more
Machine learning can be approximately equated to finding a functional function f related to specific inputs and expected outputs through statistical or inferential methods within data objects (as shown in Figure 1). Usually, we denote the input variable (feature) space as uppercase X and the output variable space as uppercase Y. Therefore, machine learning can … Read more
Machine learning is divided into two main categories based on model type: supervised learning models and unsupervised learning models: 1. Supervised Learning Supervised learning typically utilizes training data with expert-labeled tags to learn a function mapping from input variable X to output variable Y. Y = f(X), where training data is usually in the form … Read more
Source: Deephub Imba This article is about 4500 words long and is recommended for a 5-minute read. This article describes a method for training high-quality, transferable embeddings using self-supervised learning techniques on user search data from within the website. The number of categories in large websites is vast, and manual tagging is generally unfeasible. Therefore, … Read more