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
Selected from Kdnuggets Author:Lars Hulstaert Translated by Machine Heart Contributors: Yan Qi, Li Zenan This article is aimed at slightly experienced machine learning developers. Lars Hulstaert from Microsoft introduces several objective functions for training neural networks. Introduction The motivation for writing this article has three aspects: First, there are many articles introducing optimization methods, such … Read more
Core Points:A comprehensive summary of machine learning concepts, highly recommended for collection! Hi, I am Cos Dazhuang! Machine learning is 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 … Read more
Machine learning is 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), and the training data is usually in the … Read more
Self-Supervised Learning is a popular research area in recent years. It aims to extract the inherent representation features of unlabeled data by designing auxiliary tasks as supervisory signals, thereby enhancing the model’s feature extraction capabilities. Today, let’s explore what self-supervised learning is! 01 What is Self-Supervised Learning? Machine learning can be classified into supervised learning, … Read more
Reported by Machine Heart In today’s NLP field, we can see the success of “Transformer-based Pre-trained Language Models (T-PTLM)” in almost every task. These models originated from GPT and BERT. The technical foundations of these models include Transformer, self-supervised learning, and transfer learning. T-PTLM can learn universal language representations from large-scale text data using self-supervised … Read more
Click the above“Beginner’s Visual Learning” to selectStar or “Top” Heavyweight content delivered promptly Introduction The KNN algorithm, or K-Nearest Neighbors algorithm, is a type of supervised learning algorithm that essentially finds the K instances closest to a given test sample A among the provided training samples, and then counts the most frequent class among these … Read more
（Cover and body images are from the internet） Machine learning may be a “familiar yet strange term”. You often hear people mention it, but its specific meaning is not very clear. When we talk about machine learning, what are we actually discussing? This article will organize the discussion from four aspects: / What is Machine … Read more