Understanding Objective Functions in Neural Networks

Understanding Objective Functions in Neural Networks

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

Comprehensive Summary of Machine Learning Concepts

Comprehensive Summary of Machine Learning Concepts

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

Comprehensive Summary of Machine Learning Basics

Comprehensive Summary of Machine Learning Basics

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

Understanding Self-Supervised Learning

Understanding Self-Supervised Learning

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

Overview of Latest Transformer Pre-training Models

Overview of Latest Transformer Pre-training Models

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

KNN Learning for Image Classification and Principles

KNN Learning for Image Classification and Principles

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

What We Mean When We Talk About Machine Learning

What We Mean When We Talk About Machine Learning

(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

Understanding Machine Learning Through Visuals

Understanding Machine Learning Through Visuals

Source: DeepHub IMBA This article is about 2300 words long and is recommended for an 8-minute read. This article introduces the types of machine learning. Machine Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning can … Read more

Comprehensive Summary of Machine Learning Basics

Comprehensive Summary of Machine Learning Basics

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), where training data is usually in the form … Read more

17 Common Algorithms in Machine Learning

17 Common Algorithms in Machine Learning

Reprinted from Algorithms and the Beauty of Mathematics 1. Supervised Learning: In supervised learning, the input data is referred to as “training data”, and each set of training data has a clear label or result, such as “spam” or “non-spam” in a spam detection system, or the digits “1”, “2”, “3”, “4”, etc. in handwritten … Read more