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loss functions

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Model Perspective
Model Perspective
Aug 25, 2023 · Artificial Intelligence

Understanding Common Loss Functions Across Machine Learning Models

This article explains the purpose of loss functions in machine learning and reviews the specific loss functions used by popular algorithms such as linear regression (MSE), logistic regression (cross‑entropy), decision trees, random forests, SVM (hinge loss), neural networks, and AdaBoost (exponential loss).

AIAlgorithmsMachine Learning
0 likes · 3 min read
Understanding Common Loss Functions Across Machine Learning Models
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Jul 29, 2023 · Artificial Intelligence

Introduction to Machine Learning: Concepts, Terminology, Algorithms, Evaluation Metrics, and Practical Code Examples

This article provides a comprehensive overview of machine learning, covering fundamental concepts, key terminology, common algorithms for supervised, unsupervised, and reinforcement learning, model evaluation metrics, loss functions, and practical code examples such as random forest and SVM implementations.

AlgorithmsMachine Learningloss functions
0 likes · 35 min read
Introduction to Machine Learning: Concepts, Terminology, Algorithms, Evaluation Metrics, and Practical Code Examples
Python Programming Learning Circle
Python Programming Learning Circle
Jun 12, 2023 · Artificial Intelligence

10 Common Loss Functions and Their Python Implementations

This article explains ten widely used loss functions for regression and classification tasks, describes their mathematical definitions, compares their purposes, and provides complete Python code examples for each, helping readers understand how to select and implement appropriate loss metrics in machine‑learning models.

AIMachine Learningclassification
0 likes · 10 min read
10 Common Loss Functions and Their Python Implementations
Bilibili Tech
Bilibili Tech
Nov 8, 2022 · Artificial Intelligence

Real-Time Super-Resolution Algorithm for League of Legends S12 Live Streaming

A lightweight real‑time super‑resolution network was created for the 2022 League of Legends S12 World Championship, using pixel‑unshuffle/shuffle, structural re‑parameterization, and a multi‑loss (L1, perceptual, Sobel‑based texture, GAN) training pipeline that upscales 1080p streams to 4K at 75 fps on a V100 GPU, delivering clearer textures and reduced noise while remaining computationally efficient.

deep learninggame streamingloss functions
0 likes · 10 min read
Real-Time Super-Resolution Algorithm for League of Legends S12 Live Streaming
Bilibili Tech
Bilibili Tech
Oct 18, 2022 · Artificial Intelligence

Real-Time Super-Resolution Algorithm for League of Legends S12 Live Streaming

A real‑time super‑resolution network specially designed for the League of Legends S12 live broadcast upscales 1080p streams to 4K at 75 fps by compressing parameters, employing pixel‑unshuffle/shuffle, structural re‑parameterization, and a multi‑loss (L1, perceptual, Sobel, GAN) training pipeline, delivering markedly sharper textures and lower latency for live game streaming.

deep learninggame streamingloss functions
0 likes · 12 min read
Real-Time Super-Resolution Algorithm for League of Legends S12 Live Streaming
Liulishuo Tech Team
Liulishuo Tech Team
May 20, 2022 · Artificial Intelligence

Multi‑Scale BERT‑Based Automated Essay Scoring: Architecture, Loss Functions, and Experimental Evaluation

This article surveys automated essay scoring (AES), compares handcrafted, deep‑learning, and pre‑trained language‑model approaches, proposes a multi‑scale BERT architecture with document, token, and segment features, introduces three combined loss functions, and demonstrates superior performance on the ASAP dataset and internal tasks.

ASAP datasetBERTartificial intelligence
0 likes · 13 min read
Multi‑Scale BERT‑Based Automated Essay Scoring: Architecture, Loss Functions, and Experimental Evaluation
DataFunTalk
DataFunTalk
Apr 5, 2021 · Artificial Intelligence

Summary of Methods and Findings from the NLP Chinese Pre‑training Model Generalization Challenge

The article reviews the Chinese NLP pre‑training model generalization competition, detailing data preprocessing, augmentation, external data usage, model scaling and architecture tweaks, loss functions, learning‑rate and adversarial training strategies, regularization techniques, post‑processing optimizations, and ineffective methods, highlighting their impact on performance metrics.

NLPdata augmentationloss functions
0 likes · 15 min read
Summary of Methods and Findings from the NLP Chinese Pre‑training Model Generalization Challenge