"모델 성능이 안 나올 때 첫 번째로 의심해야 할 것!" MSE vs MAE vs Huber Loss, Binary vs Categorical Cross-Entropy, Focal Loss, Dice Loss까지. 회귀/분류 문제별 최적 손실함수 선택법, PyTorch/TensorFlow 구현 코드, 실전 문제 해결 전략까지 완벽 가이드.
#CrossEntropy #FocalLoss #LossFunction #MSE #PyTorch #TensorFlow #딥러닝러닝 #머신러닝��러닝
doyouknow.kr/761/loss-fun...
December 7, 2025 at 3:13 PM
"모델 성능이 안 나올 때 첫 번째로 의심해야 할 것!" MSE vs MAE vs Huber Loss, Binary vs Categorical Cross-Entropy, Focal Loss, Dice Loss까지. 회귀/분류 문제별 최적 손실함수 선택법, PyTorch/TensorFlow 구현 코드, 실전 문제 해결 전략까지 완벽 가이드.
#CrossEntropy #FocalLoss #LossFunction #MSE #PyTorch #TensorFlow #딥러닝러닝 #머신러닝��러닝
doyouknow.kr/761/loss-fun...
Explore a curated list of influential academic references covering the history and modern developments in empirical Bayes and panel data econometrics #lossfunction
The Evolution of Econometric Modeling: A Guide to Influential Papers on Panel Data
hackernoon.com
September 10, 2025 at 3:30 PM
Explore a curated list of influential academic references covering the history and modern developments in empirical Bayes and panel data econometrics #lossfunction
This article explores the concept of regret in empirical Bayes, specifically in the context of the Tweedie oracle rule. #lossfunction
The Tweedie Oracle and Regret Bounds in Empirical Bayes Methods
hackernoon.com
September 10, 2025 at 2:36 AM
This article explores the concept of regret in empirical Bayes, specifically in the context of the Tweedie oracle rule. #lossfunction
A flexible approach that allows for nonparametric estimation of location and scale effects, providing a more reliable way to predict income trajectories #lossfunction
Modeling Income Trajectories: An Empirical Bayes Approach to Panel Data
hackernoon.com
September 9, 2025 at 2:15 PM
A flexible approach that allows for nonparametric estimation of location and scale effects, providing a more reliable way to predict income trajectories #lossfunction
Discussing key concepts like partial identification and the transformation of binomial models to the Gaussian framework. #lossfunction
Beyond Gaussian Mixtures: Applying Empirical Bayes to Discrete Data Problems
hackernoon.com
September 9, 2025 at 1:00 PM
Discussing key concepts like partial identification and the transformation of binomial models to the Gaussian framework. #lossfunction
This article explores the history and modern developments of nonparametric maximum likelihood estimation (NPMLE) for mixture models. #lossfunction
Nonparametric Maximum Likelihood Estimation: A Practical Guide to Mixture Models
hackernoon.com
September 9, 2025 at 2:03 AM
This article explores the history and modern developments of nonparametric maximum likelihood estimation (NPMLE) for mixture models. #lossfunction
This article explores variations on the James-Stein estimator, focusing on the Efron-Morris rule and its implications for shrinkage in statistical analysis. #lossfunction
The Efron-Morris Rule: A Practical Application of the Empirical Bayes Paradigm
hackernoon.com
September 9, 2025 at 1:34 AM
This article explores variations on the James-Stein estimator, focusing on the Efron-Morris rule and its implications for shrinkage in statistical analysis. #lossfunction
This tutorial introduces the principles of Empirical Bayes and its frequentist interpretation, with an emphasis on modern nonparametric maximum likelihood #lossfunction
Empirical Bayes Methods for Compound Decision Problems: Principles and Applications
hackernoon.com
September 9, 2025 at 1:15 AM
This tutorial introduces the principles of Empirical Bayes and its frequentist interpretation, with an emphasis on modern nonparametric maximum likelihood #lossfunction
Оценка моделей глубокого обучения с пользовательскими функциями потерь и метриками калибровки
Оценка моделей глубокого обучения является неотъемлемой частью управления жизненным циклом модели. В то время как традиционные модели превосходно справлялись с предоставле…
#ai #deeplearning #lossfunction
Оценка моделей глубокого обучения является неотъемлемой частью управления жизненным циклом модели. В то время как традиционные модели превосходно справлялись с предоставле…
#ai #deeplearning #lossfunction
Evaluating Deep Learning Models with Custom Loss Functions and Calibration Metrics
www.analyticsvidhya.com
August 18, 2025 at 12:53 AM
Оценка моделей глубокого обучения с пользовательскими функциями потерь и метриками калибровки
Оценка моделей глубокого обучения является неотъемлемой частью управления жизненным циклом модели. В то время как традиционные модели превосходно справлялись с предоставле…
#ai #deeplearning #lossfunction
Оценка моделей глубокого обучения является неотъемлемой частью управления жизненным циклом модели. В то время как традиционные модели превосходно справлялись с предоставле…
#ai #deeplearning #lossfunction
Evaluating Deep Learning Models with Custom Loss Functions and Calibration Metrics
Evaluating Deep Learning models is an essential part of model lifecycle management. Whereas traditional models have excelled at providing quick benchmarks for model performance, they…
#ai #deeplearning #lossfunction
Evaluating Deep Learning models is an essential part of model lifecycle management. Whereas traditional models have excelled at providing quick benchmarks for model performance, they…
#ai #deeplearning #lossfunction
Evaluating Deep Learning Models with Custom Loss Functions and Calibration Metrics
Evaluating Deep Learning models is an essential part of model lifecycle management. Whereas traditional models have excelled at providing quick benchmarks for model performance, they often fail to capture the nuanced goals of real-world applications. For instance, a fraud detection system might prioritize minimizing false negatives over false positives, while a medical diagnosis model might […]
www.analyticsvidhya.com
August 17, 2025 at 9:34 AM
Evaluating Deep Learning Models with Custom Loss Functions and Calibration Metrics
Evaluating Deep Learning models is an essential part of model lifecycle management. Whereas traditional models have excelled at providing quick benchmarks for model performance, they…
#ai #deeplearning #lossfunction
Evaluating Deep Learning models is an essential part of model lifecycle management. Whereas traditional models have excelled at providing quick benchmarks for model performance, they…
#ai #deeplearning #lossfunction
me: hello this PR adds two lines
```
# __init__
self.lossfunc = LossFunction()
...
# forward
loss = self.lossfunc(logits, targets)
```
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```
# __init__
self.lossfunc = LossFunction()
...
# forward
loss = self.lossfunc(logits, targets)
```
🤗: wow are you a wizard? 👍🚀 LGTM! we will deposit $1M HugCoin in your GitHub sponsors immediately
May 8, 2025 at 4:05 PM
me: hello this PR adds two lines
```
# __init__
self.lossfunc = LossFunction()
...
# forward
loss = self.lossfunc(logits, targets)
```
🤗: wow are you a wizard? 👍🚀 LGTM! we will deposit $1M HugCoin in your GitHub sponsors immediately
```
# __init__
self.lossfunc = LossFunction()
...
# forward
loss = self.lossfunc(logits, targets)
```
🤗: wow are you a wizard? 👍🚀 LGTM! we will deposit $1M HugCoin in your GitHub sponsors immediately
Wood panel defect detection based on improved YOLOv8n
https://buff.ly/3Qg8D9a
#BioResJournal #OpenAccess #DeepLearning #YOLOv8n #ObjectDetection #woodpanel #defectdetection #lossfunction #woodprocessing #woodidentification
https://buff.ly/3Qg8D9a
#BioResJournal #OpenAccess #DeepLearning #YOLOv8n #ObjectDetection #woodpanel #defectdetection #lossfunction #woodprocessing #woodidentification
February 12, 2025 at 9:43 PM
Wood panel defect detection based on improved YOLOv8n
https://buff.ly/3Qg8D9a
#BioResJournal #OpenAccess #DeepLearning #YOLOv8n #ObjectDetection #woodpanel #defectdetection #lossfunction #woodprocessing #woodidentification
https://buff.ly/3Qg8D9a
#BioResJournal #OpenAccess #DeepLearning #YOLOv8n #ObjectDetection #woodpanel #defectdetection #lossfunction #woodprocessing #woodidentification