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Cross-entropy - Wikipedia
WEBCross-entropy can be used to define a loss function in machine learning and optimization. Mao, Mohri, and Zhong (2023) give an extensive analysis of the properties of the family of cross-entropy loss functions in machine learning, including theoretical learning guarantees and extensions to adversarial learning. [3]
En.wikipedia.orgCross-Entropy Loss Function in Machine Learning: Enhancing …
WEBCross-entropy, also known as logarithmic loss or log loss, is a popular loss function used in machine learning to measure the performance of a classification model. Namely, it measures the difference between the discovered probability distribution of a classification model and the predicted values.
Datacamp.comA Gentle Introduction to Cross-Entropy for Machine Learning
WEBDec 22, 2020 · Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information theory, building upon entropy and generally calculating the difference between two probability distributions.
Machinelearningmastery.comCrossEntropyLoss — PyTorch 2.3 documentation
WEBclass torch.nn.CrossEntropyLoss(weight=None, size_average=None, ignore_index=-100, reduce=None, reduction='mean', label_smoothing=0.0) [source] This criterion computes the cross entropy loss between input logits and target. It is useful when training a classification problem with C classes. If provided, the optional argument weight should …
Pytorch.orgA Simple Introduction to Cross Entropy Loss - Inside Learning …
WEBThe cross entropy loss is a standard evaluation function in machine learning, used to assess model performance for classification problems. This article will cover how Cross Entropy is calculated, and work through a few examples to illustrate its application in machine learning. Table of Contents. What is Cross Entropy?
Insidelearningmachines.comA Friendly Introduction to Cross-Entropy Loss - GitHub Pages
WEBIn this post we saw that cross entropy is a reasonable choice. From one perspective, minimizing cross entropy lets us find a $\hat{y}$ that requires as few extra bits as possible when we try to encode symbols from $y$ using $\hat{y}$.
Rdipietro.github.ioWhat Is Cross-Entropy Loss Function? | 365 Data Science
WEBJun 15, 2023 · The cross-entropy loss function measures your model’s performance by transforming its variables into real numbers, thereby evaluating the ’loss’ associated with them. The higher the difference between the two, the higher the loss.
365datascience.comCross-Entropy Loss Function - Towards Data Science
WEBOct 2, 2020 · Cross-entropy loss is used when adjusting model weights during training. The aim is to minimize the loss, i.e, the smaller the loss the better the model. A perfect model has a cross-entropy loss of 0.
Towardsdatascience.comCross Entropy Loss | Machine Learning Theory
WEBIn information theory, the cross-entropy $\ell_{\operatorname{CE}}(f(x),y)$ measures the distance between two distributions over the labels $(\mathcal{C}_1,\ldots,\mathcal{C}_K)$: One with the posterior probabilities $(f_1(x),\ldots,f_K(x))$; and; The other one with probabilities $(y_1,\ldots,y_K)$.
Machinelearningtheory.orgInside the maths that drives AI - Nature
WEB4 days ago · The most useful loss functions include cross entropy, a measure that compares probability distributions related to the model’s output and the real-world value (also known as maximum likelihood
Nature.com